Abhishek Kumar, PhD is an associate professor and Assistant Director in the Computer Science and Engineering Department at Chandigarh University with over 11 years of experience. He has over 100 publications in reputed, national and international journals, books, and conferences. His research interests include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning. Prasenjit Das, PhD is a professor in the Department of Computer Science and Engineering at Chandigarh University with over 19 years of experience. He has published two books, over 20 research papers, and 25 patents, three of which have been granted. His research interests include data mining, machine learning, image processing, and natural language processing. Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur with over 12 years of teaching experience. He has published over 85 papers in peer-reviewed national and international journals, books, and conferences. His research interests include networking, image processing, and machine learning. Sachin Ahuja, PhD has an illustrious academic and research career, marked by numerous impactful contributions. An accomplished editor, he has contributed to numerous high-quality academic books and served as a guest editor for special issues in reputed international journals, showcasing his expertise in emerging research domains. Additionally, he has successfully led several funded projects in advanced areas, including artificial intelligence, machine learning, and data mining, driving innovation and practical solutions. Chetan Sharma is the Program Manager at the upGrad Campus for upGradEducation Private Limited. He has published one book, over 40 research articles in national and international journals and conferences, and 30 patents, eight of which have been granted. His research interests include natural language processing, machine learning, and management.

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• Foreword xxiPreface xxiii1 Assessing Predictive Accuracy: Model Validation in Cancer Diagnostics 1M. Sudha, Arun Elias, G. Gurumoorthy, S. Rajalakshmi and S. K. Muthusundar1.1 Introduction 21.1.1 Conventional Cancer Diagnosis 31.1.2 Machine Learning in Cancer Diagnosis 31.1.3 Types of Cancer in Focus 41.1.3.1 Breast Cancer 41.1.3.2 Lung Cancer 41.1.3.3 Skin Cancer 41.1.4 Objectives of Study 41.1.5 Study Scope 51.1.6 Performance Metrics 61.1.7 Limitations and Future Directions 61.2 Literature Review 71.3 Methodology 91.3.1 Data Acquisition 91.3.2 Data Preprocessing 101.3.2.1 Dealing with Missing Data 101.3.2.2 Normalization and Standardization 101.3.2.3 Feature Selection and Dimensionality Reduction 111.3.3 Machine Learning Models 111.3.3.1 Support Vector Machine (SVM) 111.3.3.2 Random Forest (RF) 121.3.3.3 k-Nearest Neighbors (k-NN) 121.3.3.4 Logistic Regression (LR) 121.3.3.5 Hyperparameter Tuning 131.3.4 Performance Metrics 131.4 Analysis of Results 141.4.1 The Overall Performance of Each Model on the Breast Cancer Dataset 141.4.2 Models’ Performance for Lung Cancer Dataset 161.4.3 Model Performance on Skin Cancer Dataset 171.4.4 Analysis of Inter-Cancer Type Performance Comparison 181.5 Discussion of Results 191.6 Conclusion 20References 212 Applying Transfer Learning to Accelerate Cancer Classification and Prediction 23T. Ravi, Shashidhar Gurav, Nandhini, Vijayaraj and S. K. Muthusundar2.1 Introduction 242.1.1 Background on Cancer Classification 242.1.2 Transfer Learning in Medical Imaging 252.1.3 Model Development 262.2 Literature Review 272.2.1 Application of Transfer Learning in Breast Cancer Diagnosis 272.3 Methodology 302.3.1 Introduction 302.3.2 Data Preparation 312.3.2.1 Data Source 312.3.2.2 Data Collection 312.3.2.3 Data Preprocessing 312.3.2.4 Normalization 312.3.2.5 Handling Missing Values 322.3.2.6 Feature Selection 322.3.2.7 Data Partitioning 322.3.3 Model Design 332.3.3.1 Transfer Learning Approach 332.3.4 Implementation Tools 352.4 Results 362.4.1 Data Distribution 362.4.2 Accuracy, Precision, Recall, and F1-Score 372.4.3 Confusion Matrix 372.4.4 ROC Curve Analysis 392.4.5 Comparison on Traditional Machine Learning Models 392.5 Discussion of Results 402.5.1 Model Strengths 402.5.2 Areas for Improvement 402.6 Conclusion 41References 433 Artificial Intelligence in Cancer Screening: Innovations in Early Detection 45Arun Elias, V. Vaithianathan, S.K. Rajesh Kanna, G.M. Raja and S.K. Muthusundar3.1 Introduction 463.1.1 Background on Cancer Screening 463.1.2 Role of Artificial Intelligence 473.1.3 Research Methodology 473.1.4 AI in Medical Imaging 483.1.5 Challenges and Ethical Considerations 483.2 Literature Review 503.3 Methodology 533.3.1 Dataset Collection 533.3.2 Data Preprocessing 543.3.3 Architecture Model Design 553.3.4 Training and Validation 563.3.5 Metrics to Measure 573.4 Results 583.4.1 Model Performance Metrics 583.4.2 Confusion Matrix Analysis 593.4.3 Receiver Operating Characteristic Curve 603.4.4 Comparison with Existing Models 613.4.5 Error Analysis 623.5 Future Directions 623.6 Conclusion 63References 644 Comprehensive Approaches to Survival Analysis and Prognostic Modeling in Cancer Research: Integrating Statistical Techniques, and Clinical Variables 67B. Sriman, J. Maria Arockia Dass, R. Seetha and Ashish Kumar4.1 Introduction 684.1.1 Objectives 704.2 Literature Review 714.3 Methodology 744.3.1 Collection and Preprocessing 754.3.2 Cox Proportional Hazards Model 764.3.3 Random Survival Forest (RSF) 764.3.4 DeepSurv: Neural Network-Based Survival Model 774.3.5 Model Evaluation and Comparison 784.4 Results 794.4.1 General Comparison of Ability 794.4.2 Results of Cox Proportional Hazards (CPH) Model 794.4.3 RSF Results 824.4.4 Results on DeepSurv 824.4.5 Model Comparison and Discussion 844.4.6 Impact on Personalized Medicine 864.5 Conclusion 86References 875 Exploring Cancer Therapeutics: A Collection of Case Studies 89L. Selvam, Annie Silviya S. H., Singaravelan M. and Ira Aditi5.1 Introduction 905.1.1 Conventional Cancer Therapies: Limitations and Challenges 905.1.2 The New Era of Targeted Therapies 915.1.3 Immunotherapy 915.1.4 Case Study: Targeted Therapy in HER2-Positive Breast Cancer 925.1.5 Case Study: Immunotherapy in Advanced Melanoma 935.2 Literature Review 935.3 Methodology 965.3.1 Research Design 975.3.2 Patient Selection 975.3.2.1 Case Study: HER2-Positive Breast Cancer (Trastuzumab) 985.3.2.2 Advanced Melanoma Case Study (Pembrolizumab) 985.3.3 Treatment Protocols 985.3.3.1 Trastuzumab Protocol for HER2-Positive Breast Cancer 995.3.4 Data Collection 995.3.4.1 Clinical and Imaging Data 1005.3.4.2 Immune and Genetic Markers 1005.3.5 Statistical Analysis 1005.4 Results 1015.4.1 Tumor Response 1015.4.2 Survival Analysis 1035.4.3 Recurrence Rate and Disease Control 1055.4.4 Immune-Related Adverse Events and Safety Profile 1065.5 Conclusion 109References 1096 Predicting Cancer Outcomes Using Transfer Learning: Harnessing Pre-Trained Models and Cross-Domain Knowledge for Enhanced Prognosis and Personalized Treatment Strategies 111R. Ramachandran, V. Vaissnave, Vijayaraj and S. K. Muthusundar6.1 Introduction 1126.1.1 Background 1126.1.2 Objectives 1136.2 Literature Review 1146.3 Methodology 1196.3.1 Data Collection 1196.3.2 Preprocessing the Data 1206.3.3 Modeling 1216.3.4 Model Assessment 1226.3.5 Implementation of the Integrated Model 1226.4 Results 1236.4.1 Model Performance Metrics 1236.4.2 Baseline Model Comparisons 1256.4.3 Feature Importance Analysis 1256.4.4 Clinical Validation Results 1276.5 Conclusion 128References 1297 Predicting Cancer Outcomes with RNNs: A Time Series Approach 133M. Mahalakshmi, Annie Silviya S. H., Kumud Sachdeva and Rajan Sachdeva7.1 Introduction 1347.1.1 Background 1347.1.2 Significance of Ensemble Learning 1347.1.3 Objectives 1357.1.4 Significance of the Study 1367.2 Literature Review 1367.3 Methodology 1377.3.1 Objective 1377.3.2 Data Collection 1377.3.2.1 Dataset 1377.3.3 Preprocessing 1387.3.3.1 Data Drawing 1387.3.3.2 Normalization Numerical Features 1387.3.3.3 Point Selection 1397.3.4 Feature Selection 1397.3.5 Ensemble Learning Techniques 1407.3.6 Model Evaluation Metrics 1427.3.7 Cross-Validation 1437.4 Results 1437.4.1 Model Performance 1437.5 Results 1497.5.1 Cross-Validation Results 1497.5.2 Model Comparison 1497.6 Conclusion 151References 1518 AI in Cancer Screening and Early Detection 153Priya Batta and Soumen Sardar8.1 Introduction 1538.2 Literature Review 1578.3 Methodology 1608.4 Results 1628.5 Conclusion and Future Scope 163References 1649 Challenges and Limitations of AI in Oncology 167Priya Batta9.1 Introduction 1679.2 Literature Review 1709.3 Methodology 1729.4 Results 1749.5 Conclusion and Future Scope 174References 17510 Predictive Models for Cancer-Related Lymphedema: Enhancing Telerehabilitation and Physiotherapy Management 177Madhusmita Jena, Charu Chhabra, Huma Parveen, Sahar Zaidi, Noor Fatima and Habiba Sundus10.1 Introduction 17810.1.1 Prevalence of Lymphedema 17910.1.2 Diagnostic Technique for Lymphedema 18010.1.3 Commonly Used Scales for Diagnosis of Lymphedema 18110.2 Lymphedema’s Impact on Cancer Survivors 18110.3 Current Challenges in Lymphedema Management 18210.4 Role of AI in Lymphedema Management 18310.4.1 Customizing Physiotherapy Regimens Based on AI Predictions 18410.4.2 Integrating Telerehabilitation for Effective Lymphedema Management 18510.5 Conclusion 186References 18611 Role of AI in the Prediction of Leukemia and AI-Driven Predictive Models for Rehabilitation Outcomes in Acute Lymphoblastic Leukemia 189Huma Parveen, Charu Chhabra, Sahar Zaidi, Noor Fatima, Madhusmita Jena and Amaan Ali Khan11.1 Acute Lymphoblastic Leukemia 19011.2 Importance of Early Prediction and Rehabilitation in ALL 19111.3 Role of AI in Healthcare 19311.4 AI in Leukemia Prediction 19411.5 AI-Driven Predictive Rehabilitation Outcomes in ALL 19611.6 Data Privacy and Security in Healthcare Models 19911.7 Framework for Protecting Data Privacy 20011.7.1 Acts and Policies 20011.7.2 National Policies 20011.7.3 AI Models-Based Privacy Protection 20111.8 Ethical Concerns in AI Healthcare 202References 20312 Data Privacy and Ethical Challenges in AI-Driven Cancer Care 207Firdaus Jawed, Rabia Aziz, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan12.1 Introduction to Data Privacy and Ethics in AI-Driven Cancer Care 20812.2 Types of Sensitive Data in AI-Driven Cancer Care 20912.3 Ethical Frameworks and Guidelines for Data Privacy 21212.4 Data Security and Protection Techniques 21412.5 Bias, Fairness, and Algorithmic Transparency in AI-Driven Cancer Care 21612.6 Regulatory and Compliance Challenges 21912.7 Emerging Technologies and Innovations in Privacy 22112.8 Future Directions in Ethical AI for Cancer Care 22212.9 Conclusions 224References 22413 Cancer Rehabilitation in the Era of Targeted Chemotherapy and Personalized Immunotherapy 229Rabia Aziz, Firdaus Jawed, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan13.1 Evolving Landscape of Cancer Treatment 23013.2 Importance of Cancer Rehabilitation 23113.3 Integrating Rehabilitation Into AI-Powered Cancer Rehabilitation 23213.3.1 The Role of Data in Rehabilitation 23813.3.2 Machine Learning and Predictive Analytics 23913.3.3 Real-Time Monitoring and Feedback 23913.3.4 Outcomes Measurement and Continuous Improvement 24013.3.5 The Rationale for Integration 24113.3.6 Utilizing Biomarkers in Rehabilitation 24113.3.7 Multidisciplinary Collaboration 24213.3.8 Early Intervention Strategies 24213.3.9 Leveraging Technology for Monitoring and Feedback 24313.4 Leveraging Data Analytics and AI for Adaptive Rehabilitation 24313.4.1 The Role of Data Analytics in Rehabilitation 24413.4.2 AI-Driven Personalization of Rehabilitation Programs 24413.4.3 Integration of Wearable Technology and Telehealth 24513.4.4 Virtual Reality (VR) and Augmented Reality (AR) Applications 24513.5 Tailoring Rehabilitation Strategies for Targeted Therapies 24613.5.1 Understanding Targeted Therapies and Their Implications 24613.5.2 Personalized Assessment and Planning 24713.5.3 Integrating Evidence-Based Interventions 24713.5.3.1 Physical Therapy 24713.5.3.2 Occupational Therapy 24813.5.3.3 Psychosocial Support 24913.5.3.4 Nutritional Counseling 24913.5.4 Utilizing Technology for Enhanced Rehabilitation 25013.6 Future Directions and Emerging Trends 25113.7 Summary 251References 25214 Role of AI in Cancer Screening and Its Detection 257Muskan, Shweta Sharma, Parul Sharma, Manoj Malik and Jaspreet Kaur14.1 Introduction 25814.2 Cancer Mechanisms and Various Pathologies 25814.3 Conventional Methods of Cancer Screening 26014.3.1 Mammography 26014.3.2 Ultrasound 26214.3.3 Magnetic Resonance Imaging 26214.3.4 Liquid Biopsies 26214.3.5 Pap Smear (Papanicolaou Test) 26314.3.6 Barium X-Ray (Barium Swallow or Enema) 26314.3.7 Photoacoustic Tomography (PAT) 26314.3.8 SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography) 26314.4 Overview of AI (Artificial Intelligence) in Cancer Detection 26414.5 AI Applications in Cancer Screening Using Deep Learning and Machine Learning 26614.5.1 AI Models for Breast Cancer 26614.5.2 AI Models for Lung Cancer 26614.5.3 AI Models for Skin Cancer 26714.5.4 AI Models for Gastric Cancers 26714.5.5 AI Models for Prostate Cancers 26914.6 Challenges in AI Adoption for Cancer Screening 27014.7 Proposed Strategies for AI Implementation for Cancer Detection 27114.8 Conclusion 27214.9 Future Directions 273References 27415 Automated 3D U-Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI-Driven Cancer Research Applications 279S. Usharani, P. Manju Bala, T. Ananth kumar and G. Glorindal Selvam15.1 Introduction 28015.2 Literature Review 28315.2.1 Brain Tumor MRI Image Segmentation 28315.2.1.1 Methods for Manual Segmentation 28315.2.1.2 Methods for Partly-Automated Segmentation 28315.2.1.3 Methods for Absolutely Automated Segmentation 28415.2.2 Brain Tumor MRI Classification 28515.3 Materials and Methods 29015.3.1 Materials 29015.3.2 Methods 29115.3.2.1 System Model 29115.3.2.2 Multi Scale Feature Extraction Network 29315.3.2.3 Incremental Feature Improvement 29415.3.2.4 Loss Function 29515.4 Experimental Setup 29615.4.1 Experimental Analysis 29615.5 Conclusion 301References 30216 Early Prediction of Bone Cancer: Integrating Deep Learning Models 309R. Dhinesh, T. Ananth kumar, P. Kanimozhi and Sunday Adeola Ajagbe16.1 Introduction 31016.2 Related Works 31116.3 Proposed Methodology 31316.4 Results and Discussion 31816.5 Conclusion 321References 32217 Machine Learning Techniques for Predicting Epileptic Seizures: A Data-Driven Analysis Using EEG Signals 325Preeti Narooka, Ankit Vishnoi and Jatin Verma17.1 Introduction 32617.1.1 Background 32617.1.2 Objective 32617.2 Literature Survey 32717.2.1 Study 1: Feature Extraction Techniques in EEG-Based Seizure Detection 32717.2.2 Study 2: Application of Deep Learning in Neurological Disorders 32717.2.3 Study 3: Comparative Analysis of ML Algorithms 32817.2.4 Study 4: Transfer Learning in EEG Analysis 32817.2.5 Study 5: Real-Time Seizure Prediction Systems 32817.2.6 Study 6: Explainable Artificial Intelligence in Seizure Detection 32817.2.7 Study 7: Challenges in EEG-Based Seizure Detection 32917.2.8 Study 8: Multimodal Learning Approaches 32917.3 Methodology 32917.3.1 Dataset 32917.3.2 Preprocessing 33017.3.3 Feature Extraction 33017.3.4 Model Architecture 33117.4 Results and Discussion 33217.4.1 Model Performance 33217.4.2 Discussion 33317.4.3 Implications for Healthcare Applications 33417.5 Conclusion 334References 33418 Transfer Learning in Cancer Research 337Mamta and Nitin18.1 Definition and Overview of Transfer Learning 33818.1.1 Transfer Learning Typically Involves the Following Components 33818.1.2 Importance of Transfer Learning in Cancer Research 33918.1.3 Challenges in Traditional Cancer Research Approaches 34118.2 How Transfer Learning Works 34418.2.1 Types of Transfer Learning 34518.2.2 Transductive Transfer Learning 34618.3 Applications of Transfer Learning in Cancer Research 34618.4 Challenges in Transfer Learning for Cancer 34818.4.1 Data Scarcity and Domain Adaptation 34818.4.2 Model Interpretability 34918.5 Future Directions: Personalized Medicine and Drug Discovery 35118.5.1 Personalized Medicine: Tailoring Treatment to the Individual 35218.6 Drug Discovery: Accelerating the Path to New Therapies 35318.7 Challenges and Ethical Considerations 35418.8 Conclusion 354References 35519 Machine Learning Approaches for Early Detection of Cervical Cancer: A Comparative Study of Classification Models 359Inam Ul Haq, Janvi Malhotra, Vanshika Rawat, Jyoti Kumari and Gagandeep Kaur19.1 Introduction 36019.2 Literature Review of Some Research Papers 36419.3 Methodology 36819.4 Results 36919.5 Conclusion and Future Scope 370References 37020 Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data 375M. Rohini, S. Oswalt Manoj, J. P. Ananth and D. Surendran20.1 Introduction 37620.1.1 Need of Electronic Health Record Maintenance 37620.1.2 Message Passing Protocol for Cancer EHR Updates 37720.1.3 Reliable Messaging for Critical Data 37920.1.4 Microservice-Oriented Cancer Data Staging and Deployment 38020.2 HER Data Processing 38220.2.1 Staging Service 38220.2.1.1 Autoscaling Based on Criticality of EHR System 38220.2.2 Internal Working of the Staging Service 38320.2.2.1 Validate and Fetch Dashboard Details 38320.2.2.2 Execute Stored Procedure 38520.2.2.3 High-Availability Deployment Phase 38620.3 Conclusion 388References 38921 Artificial Intelligence–Driven Personalized Cancer Treatment 391Gurwinder Singh, Sarthak Sharma and Aastha Anand21.1 Introduction: The Dawn of Artificial Intelligence–Powered Cancer Screening 39221.2 Role of AI in Cancer Screening 39521.3 Role of AI in Early Detection 39921.4 Case Studies and Real-World Implementation 40121.5 Benefits and Opportunities 40421.6 Conclusion 40621.7 Future Scope 407References 40822 Revolutionizing Breast Cancer Detection: Emerging Trends and Future Technologies 411Gurmeet Kaur Saini, Inderdeep Kaur and Kanwaldeep Kaur22.1 Overview 41222.2 Risk Assessment Types 41322.3 Risk Elements 41322.4 Risk Factors for Hormones and Reproduction 41322.5 Additional Risk Factors 41422.6 Risk of Breast Cancer Over Time 41422.6.1 Risk Assessment by Family History 41422.7 Models for Risk Estimate 41522.7.1 The Gail Model 41522.7.2 Claus–Mammary Carcinoma Risk Assessment Model 41622.7.3 The BRCAPRO Model 41822.7.4 Tools for Risk Calculation 41822.8 Clinical Breast Imaging Techniques 41822.8.1 Mammography 41822.8.2 Ultrasonic 42022.8.3 Magnetic Resonance Imaging 42022.9 Measurement Systems and Techniques for Microwave Breast Imaging 42122.9.1 Tomography Using Microwaves 42122.9.2 Microwave Imaging Using Radar Technology 42122.9.3 Breast Cancer Detection Using Biosensors 42222.9.4 Use of Thermography to Find Breast Cancer 42222.10 Discussion 42322.11 Present Developments and Prospects for Breast Cancer Screening Methods 42322.12 Conclusion 426References 42623 Future of Neurological Research: Leveraging Artificial Intelligence for Precision and Discovery 431Hemlata and Utsav Krishan Murari23.1 Introduction 43123.2 AI in Neuroimaging: A Revolution in Neurological Research 43523.3 Computational Neuroscience and Modeling: Transforming Understanding of Neural Mechanisms through AI 43823.4 AI and BCIs: Transforming Accessibility and Real-Time Neural Interaction 44123.5 Ethics in the Integration of AI Into Neurological Research 44523.6 Conclusion 448References 44924 Cervical Cancer Detection Using Machine Learning 451Saranya. A., S. Ravi, Harsha Latha. P., T. Kalaichelvi and A. Anbarasi24.1 Introduction 45224.1.1 Overview of Medical Image Analysis 45424.2 ml Techniques for Cervical Cancer Diagnosis 45924.2.1 ml Algorithms 45924.2.2 Methodology of ML Classification of Images 46024.2.3 Cervical Cancer Image Dataset 46224.3 Related Work 46224.3.1 Cervical Cancer Detection Using ml 46324.4 Findings 46624.5 Performance Metrics in ml 47024.6 Conclusion 471References 47225 Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer 477Saranya. A., S. Ravi, Harsha Latha. P. and T. Kalaichelvi25.1 Introduction 47825.2 Motivation of Computer-Aided Diagnosis 48025.3 History of DL in Medical Imaging 48225.4 Deep Learning Application of Cervical Cancer 48225.5 Cervical Cancer Detection Based on DL Techniques for Medical Image Segmentation 48325.5.1 Deep Learning in Image Segmentation 48325.5.2 Deep Learning in Classification Task 48725.6 Frameworks Used in Detecting Cervical Cancer 48825.6.1 Comparison Between DL Segmentation and Classification 48925.7 Performance Metrics 49425.8 Conclusion 495References 495Index 499

Abhishek Kumar, PhD is an associate professor and Assistant Director in the Computer Science and Engineering Department at Chandigarh University with over 11 years of experience. He has over 100 publications in reputed, national and international journals, books, and conferences. His research interests include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning. Prasenjit Das, PhD is a professor in the Department of Computer Science and Engineering at Chandigarh University with over 19 years of experience. He has published two books, over 20 research papers, and 25 patents, three of which have been granted. His research interests include data mining, machine learning, image processing, and natural language processing. Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur with over 12 years of teaching experience. He has published over 85 papers in peer-reviewed national and international journals, books, and conferences. His research interests include networking, image processing, and machine learning. Sachin Ahuja, PhD has an illustrious academic and research career, marked by numerous impactful contributions. An accomplished editor, he has contributed to numerous high-quality academic books and served as a guest editor for special issues in reputed international journals, showcasing his expertise in emerging research domains. Additionally, he has successfully led several funded projects in advanced areas, including artificial intelligence, machine learning, and data mining, driving innovation and practical solutions. Chetan Sharma is the Program Manager at the upGrad Campus for upGradEducation Private Limited. He has published one book, over 40 research articles in national and international journals and conferences, and 30 patents, eight of which have been granted. His research interests include natural language processing, machine learning, and management.

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• Foreword xxiPreface xxiii1 Assessing Predictive Accuracy: Model Validation in Cancer Diagnostics 1M. Sudha, Arun Elias, G. Gurumoorthy, S. Rajalakshmi and S. K. Muthusundar1.1 Introduction 21.1.1 Conventional Cancer Diagnosis 31.1.2 Machine Learning in Cancer Diagnosis 31.1.3 Types of Cancer in Focus 41.1.3.1 Breast Cancer 41.1.3.2 Lung Cancer 41.1.3.3 Skin Cancer 41.1.4 Objectives of Study 41.1.5 Study Scope 51.1.6 Performance Metrics 61.1.7 Limitations and Future Directions 61.2 Literature Review 71.3 Methodology 91.3.1 Data Acquisition 91.3.2 Data Preprocessing 101.3.2.1 Dealing with Missing Data 101.3.2.2 Normalization and Standardization 101.3.2.3 Feature Selection and Dimensionality Reduction 111.3.3 Machine Learning Models 111.3.3.1 Support Vector Machine (SVM) 111.3.3.2 Random Forest (RF) 121.3.3.3 k-Nearest Neighbors (k-NN) 121.3.3.4 Logistic Regression (LR) 121.3.3.5 Hyperparameter Tuning 131.3.4 Performance Metrics 131.4 Analysis of Results 141.4.1 The Overall Performance of Each Model on the Breast Cancer Dataset 141.4.2 Models’ Performance for Lung Cancer Dataset 161.4.3 Model Performance on Skin Cancer Dataset 171.4.4 Analysis of Inter-Cancer Type Performance Comparison 181.5 Discussion of Results 191.6 Conclusion 20References 212 Applying Transfer Learning to Accelerate Cancer Classification and Prediction 23T. Ravi, Shashidhar Gurav, Nandhini, Vijayaraj and S. K. Muthusundar2.1 Introduction 242.1.1 Background on Cancer Classification 242.1.2 Transfer Learning in Medical Imaging 252.1.3 Model Development 262.2 Literature Review 272.2.1 Application of Transfer Learning in Breast Cancer Diagnosis 272.3 Methodology 302.3.1 Introduction 302.3.2 Data Preparation 312.3.2.1 Data Source 312.3.2.2 Data Collection 312.3.2.3 Data Preprocessing 312.3.2.4 Normalization 312.3.2.5 Handling Missing Values 322.3.2.6 Feature Selection 322.3.2.7 Data Partitioning 322.3.3 Model Design 332.3.3.1 Transfer Learning Approach 332.3.4 Implementation Tools 352.4 Results 362.4.1 Data Distribution 362.4.2 Accuracy, Precision, Recall, and F1-Score 372.4.3 Confusion Matrix 372.4.4 ROC Curve Analysis 392.4.5 Comparison on Traditional Machine Learning Models 392.5 Discussion of Results 402.5.1 Model Strengths 402.5.2 Areas for Improvement 402.6 Conclusion 41References 433 Artificial Intelligence in Cancer Screening: Innovations in Early Detection 45Arun Elias, V. Vaithianathan, S.K. Rajesh Kanna, G.M. Raja and S.K. Muthusundar3.1 Introduction 463.1.1 Background on Cancer Screening 463.1.2 Role of Artificial Intelligence 473.1.3 Research Methodology 473.1.4 AI in Medical Imaging 483.1.5 Challenges and Ethical Considerations 483.2 Literature Review 503.3 Methodology 533.3.1 Dataset Collection 533.3.2 Data Preprocessing 543.3.3 Architecture Model Design 553.3.4 Training and Validation 563.3.5 Metrics to Measure 573.4 Results 583.4.1 Model Performance Metrics 583.4.2 Confusion Matrix Analysis 593.4.3 Receiver Operating Characteristic Curve 603.4.4 Comparison with Existing Models 613.4.5 Error Analysis 623.5 Future Directions 623.6 Conclusion 63References 644 Comprehensive Approaches to Survival Analysis and Prognostic Modeling in Cancer Research: Integrating Statistical Techniques, and Clinical Variables 67B. Sriman, J. Maria Arockia Dass, R. Seetha and Ashish Kumar4.1 Introduction 684.1.1 Objectives 704.2 Literature Review 714.3 Methodology 744.3.1 Collection and Preprocessing 754.3.2 Cox Proportional Hazards Model 764.3.3 Random Survival Forest (RSF) 764.3.4 DeepSurv: Neural Network-Based Survival Model 774.3.5 Model Evaluation and Comparison 784.4 Results 794.4.1 General Comparison of Ability 794.4.2 Results of Cox Proportional Hazards (CPH) Model 794.4.3 RSF Results 824.4.4 Results on DeepSurv 824.4.5 Model Comparison and Discussion 844.4.6 Impact on Personalized Medicine 864.5 Conclusion 86References 875 Exploring Cancer Therapeutics: A Collection of Case Studies 89L. Selvam, Annie Silviya S. H., Singaravelan M. and Ira Aditi5.1 Introduction 905.1.1 Conventional Cancer Therapies: Limitations and Challenges 905.1.2 The New Era of Targeted Therapies 915.1.3 Immunotherapy 915.1.4 Case Study: Targeted Therapy in HER2-Positive Breast Cancer 925.1.5 Case Study: Immunotherapy in Advanced Melanoma 935.2 Literature Review 935.3 Methodology 965.3.1 Research Design 975.3.2 Patient Selection 975.3.2.1 Case Study: HER2-Positive Breast Cancer (Trastuzumab) 985.3.2.2 Advanced Melanoma Case Study (Pembrolizumab) 985.3.3 Treatment Protocols 985.3.3.1 Trastuzumab Protocol for HER2-Positive Breast Cancer 995.3.4 Data Collection 995.3.4.1 Clinical and Imaging Data 1005.3.4.2 Immune and Genetic Markers 1005.3.5 Statistical Analysis 1005.4 Results 1015.4.1 Tumor Response 1015.4.2 Survival Analysis 1035.4.3 Recurrence Rate and Disease Control 1055.4.4 Immune-Related Adverse Events and Safety Profile 1065.5 Conclusion 109References 1096 Predicting Cancer Outcomes Using Transfer Learning: Harnessing Pre-Trained Models and Cross-Domain Knowledge for Enhanced Prognosis and Personalized Treatment Strategies 111R. Ramachandran, V. Vaissnave, Vijayaraj and S. K. Muthusundar6.1 Introduction 1126.1.1 Background 1126.1.2 Objectives 1136.2 Literature Review 1146.3 Methodology 1196.3.1 Data Collection 1196.3.2 Preprocessing the Data 1206.3.3 Modeling 1216.3.4 Model Assessment 1226.3.5 Implementation of the Integrated Model 1226.4 Results 1236.4.1 Model Performance Metrics 1236.4.2 Baseline Model Comparisons 1256.4.3 Feature Importance Analysis 1256.4.4 Clinical Validation Results 1276.5 Conclusion 128References 1297 Predicting Cancer Outcomes with RNNs: A Time Series Approach 133M. Mahalakshmi, Annie Silviya S. H., Kumud Sachdeva and Rajan Sachdeva7.1 Introduction 1347.1.1 Background 1347.1.2 Significance of Ensemble Learning 1347.1.3 Objectives 1357.1.4 Significance of the Study 1367.2 Literature Review 1367.3 Methodology 1377.3.1 Objective 1377.3.2 Data Collection 1377.3.2.1 Dataset 1377.3.3 Preprocessing 1387.3.3.1 Data Drawing 1387.3.3.2 Normalization Numerical Features 1387.3.3.3 Point Selection 1397.3.4 Feature Selection 1397.3.5 Ensemble Learning Techniques 1407.3.6 Model Evaluation Metrics 1427.3.7 Cross-Validation 1437.4 Results 1437.4.1 Model Performance 1437.5 Results 1497.5.1 Cross-Validation Results 1497.5.2 Model Comparison 1497.6 Conclusion 151References 1518 AI in Cancer Screening and Early Detection 153Priya Batta and Soumen Sardar8.1 Introduction 1538.2 Literature Review 1578.3 Methodology 1608.4 Results 1628.5 Conclusion and Future Scope 163References 1649 Challenges and Limitations of AI in Oncology 167Priya Batta9.1 Introduction 1679.2 Literature Review 1709.3 Methodology 1729.4 Results 1749.5 Conclusion and Future Scope 174References 17510 Predictive Models for Cancer-Related Lymphedema: Enhancing Telerehabilitation and Physiotherapy Management 177Madhusmita Jena, Charu Chhabra, Huma Parveen, Sahar Zaidi, Noor Fatima and Habiba Sundus10.1 Introduction 17810.1.1 Prevalence of Lymphedema 17910.1.2 Diagnostic Technique for Lymphedema 18010.1.3 Commonly Used Scales for Diagnosis of Lymphedema 18110.2 Lymphedema’s Impact on Cancer Survivors 18110.3 Current Challenges in Lymphedema Management 18210.4 Role of AI in Lymphedema Management 18310.4.1 Customizing Physiotherapy Regimens Based on AI Predictions 18410.4.2 Integrating Telerehabilitation for Effective Lymphedema Management 18510.5 Conclusion 186References 18611 Role of AI in the Prediction of Leukemia and AI-Driven Predictive Models for Rehabilitation Outcomes in Acute Lymphoblastic Leukemia 189Huma Parveen, Charu Chhabra, Sahar Zaidi, Noor Fatima, Madhusmita Jena and Amaan Ali Khan11.1 Acute Lymphoblastic Leukemia 19011.2 Importance of Early Prediction and Rehabilitation in ALL 19111.3 Role of AI in Healthcare 19311.4 AI in Leukemia Prediction 19411.5 AI-Driven Predictive Rehabilitation Outcomes in ALL 19611.6 Data Privacy and Security in Healthcare Models 19911.7 Framework for Protecting Data Privacy 20011.7.1 Acts and Policies 20011.7.2 National Policies 20011.7.3 AI Models-Based Privacy Protection 20111.8 Ethical Concerns in AI Healthcare 202References 20312 Data Privacy and Ethical Challenges in AI-Driven Cancer Care 207Firdaus Jawed, Rabia Aziz, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan12.1 Introduction to Data Privacy and Ethics in AI-Driven Cancer Care 20812.2 Types of Sensitive Data in AI-Driven Cancer Care 20912.3 Ethical Frameworks and Guidelines for Data Privacy 21212.4 Data Security and Protection Techniques 21412.5 Bias, Fairness, and Algorithmic Transparency in AI-Driven Cancer Care 21612.6 Regulatory and Compliance Challenges 21912.7 Emerging Technologies and Innovations in Privacy 22112.8 Future Directions in Ethical AI for Cancer Care 22212.9 Conclusions 224References 22413 Cancer Rehabilitation in the Era of Targeted Chemotherapy and Personalized Immunotherapy 229Rabia Aziz, Firdaus Jawed, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan13.1 Evolving Landscape of Cancer Treatment 23013.2 Importance of Cancer Rehabilitation 23113.3 Integrating Rehabilitation Into AI-Powered Cancer Rehabilitation 23213.3.1 The Role of Data in Rehabilitation 23813.3.2 Machine Learning and Predictive Analytics 23913.3.3 Real-Time Monitoring and Feedback 23913.3.4 Outcomes Measurement and Continuous Improvement 24013.3.5 The Rationale for Integration 24113.3.6 Utilizing Biomarkers in Rehabilitation 24113.3.7 Multidisciplinary Collaboration 24213.3.8 Early Intervention Strategies 24213.3.9 Leveraging Technology for Monitoring and Feedback 24313.4 Leveraging Data Analytics and AI for Adaptive Rehabilitation 24313.4.1 The Role of Data Analytics in Rehabilitation 24413.4.2 AI-Driven Personalization of Rehabilitation Programs 24413.4.3 Integration of Wearable Technology and Telehealth 24513.4.4 Virtual Reality (VR) and Augmented Reality (AR) Applications 24513.5 Tailoring Rehabilitation Strategies for Targeted Therapies 24613.5.1 Understanding Targeted Therapies and Their Implications 24613.5.2 Personalized Assessment and Planning 24713.5.3 Integrating Evidence-Based Interventions 24713.5.3.1 Physical Therapy 24713.5.3.2 Occupational Therapy 24813.5.3.3 Psychosocial Support 24913.5.3.4 Nutritional Counseling 24913.5.4 Utilizing Technology for Enhanced Rehabilitation 25013.6 Future Directions and Emerging Trends 25113.7 Summary 251References 25214 Role of AI in Cancer Screening and Its Detection 257Muskan, Shweta Sharma, Parul Sharma, Manoj Malik and Jaspreet Kaur14.1 Introduction 25814.2 Cancer Mechanisms and Various Pathologies 25814.3 Conventional Methods of Cancer Screening 26014.3.1 Mammography 26014.3.2 Ultrasound 26214.3.3 Magnetic Resonance Imaging 26214.3.4 Liquid Biopsies 26214.3.5 Pap Smear (Papanicolaou Test) 26314.3.6 Barium X-Ray (Barium Swallow or Enema) 26314.3.7 Photoacoustic Tomography (PAT) 26314.3.8 SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography) 26314.4 Overview of AI (Artificial Intelligence) in Cancer Detection 26414.5 AI Applications in Cancer Screening Using Deep Learning and Machine Learning 26614.5.1 AI Models for Breast Cancer 26614.5.2 AI Models for Lung Cancer 26614.5.3 AI Models for Skin Cancer 26714.5.4 AI Models for Gastric Cancers 26714.5.5 AI Models for Prostate Cancers 26914.6 Challenges in AI Adoption for Cancer Screening 27014.7 Proposed Strategies for AI Implementation for Cancer Detection 27114.8 Conclusion 27214.9 Future Directions 273References 27415 Automated 3D U-Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI-Driven Cancer Research Applications 279S. Usharani, P. Manju Bala, T. Ananth kumar and G. Glorindal Selvam15.1 Introduction 28015.2 Literature Review 28315.2.1 Brain Tumor MRI Image Segmentation 28315.2.1.1 Methods for Manual Segmentation 28315.2.1.2 Methods for Partly-Automated Segmentation 28315.2.1.3 Methods for Absolutely Automated Segmentation 28415.2.2 Brain Tumor MRI Classification 28515.3 Materials and Methods 29015.3.1 Materials 29015.3.2 Methods 29115.3.2.1 System Model 29115.3.2.2 Multi Scale Feature Extraction Network 29315.3.2.3 Incremental Feature Improvement 29415.3.2.4 Loss Function 29515.4 Experimental Setup 29615.4.1 Experimental Analysis 29615.5 Conclusion 301References 30216 Early Prediction of Bone Cancer: Integrating Deep Learning Models 309R. Dhinesh, T. Ananth kumar, P. Kanimozhi and Sunday Adeola Ajagbe16.1 Introduction 31016.2 Related Works 31116.3 Proposed Methodology 31316.4 Results and Discussion 31816.5 Conclusion 321References 32217 Machine Learning Techniques for Predicting Epileptic Seizures: A Data-Driven Analysis Using EEG Signals 325Preeti Narooka, Ankit Vishnoi and Jatin Verma17.1 Introduction 32617.1.1 Background 32617.1.2 Objective 32617.2 Literature Survey 32717.2.1 Study 1: Feature Extraction Techniques in EEG-Based Seizure Detection 32717.2.2 Study 2: Application of Deep Learning in Neurological Disorders 32717.2.3 Study 3: Comparative Analysis of ML Algorithms 32817.2.4 Study 4: Transfer Learning in EEG Analysis 32817.2.5 Study 5: Real-Time Seizure Prediction Systems 32817.2.6 Study 6: Explainable Artificial Intelligence in Seizure Detection 32817.2.7 Study 7: Challenges in EEG-Based Seizure Detection 32917.2.8 Study 8: Multimodal Learning Approaches 32917.3 Methodology 32917.3.1 Dataset 32917.3.2 Preprocessing 33017.3.3 Feature Extraction 33017.3.4 Model Architecture 33117.4 Results and Discussion 33217.4.1 Model Performance 33217.4.2 Discussion 33317.4.3 Implications for Healthcare Applications 33417.5 Conclusion 334References 33418 Transfer Learning in Cancer Research 337Mamta and Nitin18.1 Definition and Overview of Transfer Learning 33818.1.1 Transfer Learning Typically Involves the Following Components 33818.1.2 Importance of Transfer Learning in Cancer Research 33918.1.3 Challenges in Traditional Cancer Research Approaches 34118.2 How Transfer Learning Works 34418.2.1 Types of Transfer Learning 34518.2.2 Transductive Transfer Learning 34618.3 Applications of Transfer Learning in Cancer Research 34618.4 Challenges in Transfer Learning for Cancer 34818.4.1 Data Scarcity and Domain Adaptation 34818.4.2 Model Interpretability 34918.5 Future Directions: Personalized Medicine and Drug Discovery 35118.5.1 Personalized Medicine: Tailoring Treatment to the Individual 35218.6 Drug Discovery: Accelerating the Path to New Therapies 35318.7 Challenges and Ethical Considerations 35418.8 Conclusion 354References 35519 Machine Learning Approaches for Early Detection of Cervical Cancer: A Comparative Study of Classification Models 359Inam Ul Haq, Janvi Malhotra, Vanshika Rawat, Jyoti Kumari and Gagandeep Kaur19.1 Introduction 36019.2 Literature Review of Some Research Papers 36419.3 Methodology 36819.4 Results 36919.5 Conclusion and Future Scope 370References 37020 Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data 375M. Rohini, S. Oswalt Manoj, J. P. Ananth and D. Surendran20.1 Introduction 37620.1.1 Need of Electronic Health Record Maintenance 37620.1.2 Message Passing Protocol for Cancer EHR Updates 37720.1.3 Reliable Messaging for Critical Data 37920.1.4 Microservice-Oriented Cancer Data Staging and Deployment 38020.2 HER Data Processing 38220.2.1 Staging Service 38220.2.1.1 Autoscaling Based on Criticality of EHR System 38220.2.2 Internal Working of the Staging Service 38320.2.2.1 Validate and Fetch Dashboard Details 38320.2.2.2 Execute Stored Procedure 38520.2.2.3 High-Availability Deployment Phase 38620.3 Conclusion 388References 38921 Artificial Intelligence–Driven Personalized Cancer Treatment 391Gurwinder Singh, Sarthak Sharma and Aastha Anand21.1 Introduction: The Dawn of Artificial Intelligence–Powered Cancer Screening 39221.2 Role of AI in Cancer Screening 39521.3 Role of AI in Early Detection 39921.4 Case Studies and Real-World Implementation 40121.5 Benefits and Opportunities 40421.6 Conclusion 40621.7 Future Scope 407References 40822 Revolutionizing Breast Cancer Detection: Emerging Trends and Future Technologies 411Gurmeet Kaur Saini, Inderdeep Kaur and Kanwaldeep Kaur22.1 Overview 41222.2 Risk Assessment Types 41322.3 Risk Elements 41322.4 Risk Factors for Hormones and Reproduction 41322.5 Additional Risk Factors 41422.6 Risk of Breast Cancer Over Time 41422.6.1 Risk Assessment by Family History 41422.7 Models for Risk Estimate 41522.7.1 The Gail Model 41522.7.2 Claus–Mammary Carcinoma Risk Assessment Model 41622.7.3 The BRCAPRO Model 41822.7.4 Tools for Risk Calculation 41822.8 Clinical Breast Imaging Techniques 41822.8.1 Mammography 41822.8.2 Ultrasonic 42022.8.3 Magnetic Resonance Imaging 42022.9 Measurement Systems and Techniques for Microwave Breast Imaging 42122.9.1 Tomography Using Microwaves 42122.9.2 Microwave Imaging Using Radar Technology 42122.9.3 Breast Cancer Detection Using Biosensors 42222.9.4 Use of Thermography to Find Breast Cancer 42222.10 Discussion 42322.11 Present Developments and Prospects for Breast Cancer Screening Methods 42322.12 Conclusion 426References 42623 Future of Neurological Research: Leveraging Artificial Intelligence for Precision and Discovery 431Hemlata and Utsav Krishan Murari23.1 Introduction 43123.2 AI in Neuroimaging: A Revolution in Neurological Research 43523.3 Computational Neuroscience and Modeling: Transforming Understanding of Neural Mechanisms through AI 43823.4 AI and BCIs: Transforming Accessibility and Real-Time Neural Interaction 44123.5 Ethics in the Integration of AI Into Neurological Research 44523.6 Conclusion 448References 44924 Cervical Cancer Detection Using Machine Learning 451Saranya. A., S. Ravi, Harsha Latha. P., T. Kalaichelvi and A. Anbarasi24.1 Introduction 45224.1.1 Overview of Medical Image Analysis 45424.2 ml Techniques for Cervical Cancer Diagnosis 45924.2.1 ml Algorithms 45924.2.2 Methodology of ML Classification of Images 46024.2.3 Cervical Cancer Image Dataset 46224.3 Related Work 46224.3.1 Cervical Cancer Detection Using ml 46324.4 Findings 46624.5 Performance Metrics in ml 47024.6 Conclusion 471References 47225 Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer 477Saranya. A., S. Ravi, Harsha Latha. P. and T. Kalaichelvi25.1 Introduction 47825.2 Motivation of Computer-Aided Diagnosis 48025.3 History of DL in Medical Imaging 48225.4 Deep Learning Application of Cervical Cancer 48225.5 Cervical Cancer Detection Based on DL Techniques for Medical Image Segmentation 48325.5.1 Deep Learning in Image Segmentation 48325.5.2 Deep Learning in Classification Task 48725.6 Frameworks Used in Detecting Cervical Cancer 48825.6.1 Comparison Between DL Segmentation and Classification 48925.7 Performance Metrics 49425.8 Conclusion 495References 495Index 499

Abhishek Kumar, PhD is an associate professor and Assistant Director in the Computer Science and Engineering Department at Chandigarh University with over 11 years of experience. He has over 100 publications in reputed, national and international journals, books, and conferences. His research interests include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning. Prasenjit Das, PhD is a professor in the Department of Computer Science and Engineering at Chandigarh University with over 19 years of experience. He has published two books, over 20 research papers, and 25 patents, three of which have been granted. His research interests include data mining, machine learning, image processing, and natural language processing. Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur with over 12 years of teaching experience. He has published over 85 papers in peer-reviewed national and international journals, books, and conferences. His research interests include networking, image processing, and machine learning. Sachin Ahuja, PhD has an illustrious academic and research career, marked by numerous impactful contributions. An accomplished editor, he has contributed to numerous high-quality academic books and served as a guest editor for special issues in reputed international journals, showcasing his expertise in emerging research domains. Additionally, he has successfully led several funded projects in advanced areas, including artificial intelligence, machine learning, and data mining, driving innovation and practical solutions. Chetan Sharma is the Program Manager at the upGrad Campus for upGradEducation Private Limited. He has published one book, over 40 research articles in national and international journals and conferences, and 30 patents, eight of which have been granted. His research interests include natural language processing, machine learning, and management.

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• Foreword xxiPreface xxiii1 Assessing Predictive Accuracy: Model Validation in Cancer Diagnostics 1M. Sudha, Arun Elias, G. Gurumoorthy, S. Rajalakshmi and S. K. Muthusundar1.1 Introduction 21.1.1 Conventional Cancer Diagnosis 31.1.2 Machine Learning in Cancer Diagnosis 31.1.3 Types of Cancer in Focus 41.1.3.1 Breast Cancer 41.1.3.2 Lung Cancer 41.1.3.3 Skin Cancer 41.1.4 Objectives of Study 41.1.5 Study Scope 51.1.6 Performance Metrics 61.1.7 Limitations and Future Directions 61.2 Literature Review 71.3 Methodology 91.3.1 Data Acquisition 91.3.2 Data Preprocessing 101.3.2.1 Dealing with Missing Data 101.3.2.2 Normalization and Standardization 101.3.2.3 Feature Selection and Dimensionality Reduction 111.3.3 Machine Learning Models 111.3.3.1 Support Vector Machine (SVM) 111.3.3.2 Random Forest (RF) 121.3.3.3 k-Nearest Neighbors (k-NN) 121.3.3.4 Logistic Regression (LR) 121.3.3.5 Hyperparameter Tuning 131.3.4 Performance Metrics 131.4 Analysis of Results 141.4.1 The Overall Performance of Each Model on the Breast Cancer Dataset 141.4.2 Models’ Performance for Lung Cancer Dataset 161.4.3 Model Performance on Skin Cancer Dataset 171.4.4 Analysis of Inter-Cancer Type Performance Comparison 181.5 Discussion of Results 191.6 Conclusion 20References 212 Applying Transfer Learning to Accelerate Cancer Classification and Prediction 23T. Ravi, Shashidhar Gurav, Nandhini, Vijayaraj and S. K. Muthusundar2.1 Introduction 242.1.1 Background on Cancer Classification 242.1.2 Transfer Learning in Medical Imaging 252.1.3 Model Development 262.2 Literature Review 272.2.1 Application of Transfer Learning in Breast Cancer Diagnosis 272.3 Methodology 302.3.1 Introduction 302.3.2 Data Preparation 312.3.2.1 Data Source 312.3.2.2 Data Collection 312.3.2.3 Data Preprocessing 312.3.2.4 Normalization 312.3.2.5 Handling Missing Values 322.3.2.6 Feature Selection 322.3.2.7 Data Partitioning 322.3.3 Model Design 332.3.3.1 Transfer Learning Approach 332.3.4 Implementation Tools 352.4 Results 362.4.1 Data Distribution 362.4.2 Accuracy, Precision, Recall, and F1-Score 372.4.3 Confusion Matrix 372.4.4 ROC Curve Analysis 392.4.5 Comparison on Traditional Machine Learning Models 392.5 Discussion of Results 402.5.1 Model Strengths 402.5.2 Areas for Improvement 402.6 Conclusion 41References 433 Artificial Intelligence in Cancer Screening: Innovations in Early Detection 45Arun Elias, V. Vaithianathan, S.K. Rajesh Kanna, G.M. Raja and S.K. Muthusundar3.1 Introduction 463.1.1 Background on Cancer Screening 463.1.2 Role of Artificial Intelligence 473.1.3 Research Methodology 473.1.4 AI in Medical Imaging 483.1.5 Challenges and Ethical Considerations 483.2 Literature Review 503.3 Methodology 533.3.1 Dataset Collection 533.3.2 Data Preprocessing 543.3.3 Architecture Model Design 553.3.4 Training and Validation 563.3.5 Metrics to Measure 573.4 Results 583.4.1 Model Performance Metrics 583.4.2 Confusion Matrix Analysis 593.4.3 Receiver Operating Characteristic Curve 603.4.4 Comparison with Existing Models 613.4.5 Error Analysis 623.5 Future Directions 623.6 Conclusion 63References 644 Comprehensive Approaches to Survival Analysis and Prognostic Modeling in Cancer Research: Integrating Statistical Techniques, and Clinical Variables 67B. Sriman, J. Maria Arockia Dass, R. Seetha and Ashish Kumar4.1 Introduction 684.1.1 Objectives 704.2 Literature Review 714.3 Methodology 744.3.1 Collection and Preprocessing 754.3.2 Cox Proportional Hazards Model 764.3.3 Random Survival Forest (RSF) 764.3.4 DeepSurv: Neural Network-Based Survival Model 774.3.5 Model Evaluation and Comparison 784.4 Results 794.4.1 General Comparison of Ability 794.4.2 Results of Cox Proportional Hazards (CPH) Model 794.4.3 RSF Results 824.4.4 Results on DeepSurv 824.4.5 Model Comparison and Discussion 844.4.6 Impact on Personalized Medicine 864.5 Conclusion 86References 875 Exploring Cancer Therapeutics: A Collection of Case Studies 89L. Selvam, Annie Silviya S. H., Singaravelan M. and Ira Aditi5.1 Introduction 905.1.1 Conventional Cancer Therapies: Limitations and Challenges 905.1.2 The New Era of Targeted Therapies 915.1.3 Immunotherapy 915.1.4 Case Study: Targeted Therapy in HER2-Positive Breast Cancer 925.1.5 Case Study: Immunotherapy in Advanced Melanoma 935.2 Literature Review 935.3 Methodology 965.3.1 Research Design 975.3.2 Patient Selection 975.3.2.1 Case Study: HER2-Positive Breast Cancer (Trastuzumab) 985.3.2.2 Advanced Melanoma Case Study (Pembrolizumab) 985.3.3 Treatment Protocols 985.3.3.1 Trastuzumab Protocol for HER2-Positive Breast Cancer 995.3.4 Data Collection 995.3.4.1 Clinical and Imaging Data 1005.3.4.2 Immune and Genetic Markers 1005.3.5 Statistical Analysis 1005.4 Results 1015.4.1 Tumor Response 1015.4.2 Survival Analysis 1035.4.3 Recurrence Rate and Disease Control 1055.4.4 Immune-Related Adverse Events and Safety Profile 1065.5 Conclusion 109References 1096 Predicting Cancer Outcomes Using Transfer Learning: Harnessing Pre-Trained Models and Cross-Domain Knowledge for Enhanced Prognosis and Personalized Treatment Strategies 111R. Ramachandran, V. Vaissnave, Vijayaraj and S. K. Muthusundar6.1 Introduction 1126.1.1 Background 1126.1.2 Objectives 1136.2 Literature Review 1146.3 Methodology 1196.3.1 Data Collection 1196.3.2 Preprocessing the Data 1206.3.3 Modeling 1216.3.4 Model Assessment 1226.3.5 Implementation of the Integrated Model 1226.4 Results 1236.4.1 Model Performance Metrics 1236.4.2 Baseline Model Comparisons 1256.4.3 Feature Importance Analysis 1256.4.4 Clinical Validation Results 1276.5 Conclusion 128References 1297 Predicting Cancer Outcomes with RNNs: A Time Series Approach 133M. Mahalakshmi, Annie Silviya S. H., Kumud Sachdeva and Rajan Sachdeva7.1 Introduction 1347.1.1 Background 1347.1.2 Significance of Ensemble Learning 1347.1.3 Objectives 1357.1.4 Significance of the Study 1367.2 Literature Review 1367.3 Methodology 1377.3.1 Objective 1377.3.2 Data Collection 1377.3.2.1 Dataset 1377.3.3 Preprocessing 1387.3.3.1 Data Drawing 1387.3.3.2 Normalization Numerical Features 1387.3.3.3 Point Selection 1397.3.4 Feature Selection 1397.3.5 Ensemble Learning Techniques 1407.3.6 Model Evaluation Metrics 1427.3.7 Cross-Validation 1437.4 Results 1437.4.1 Model Performance 1437.5 Results 1497.5.1 Cross-Validation Results 1497.5.2 Model Comparison 1497.6 Conclusion 151References 1518 AI in Cancer Screening and Early Detection 153Priya Batta and Soumen Sardar8.1 Introduction 1538.2 Literature Review 1578.3 Methodology 1608.4 Results 1628.5 Conclusion and Future Scope 163References 1649 Challenges and Limitations of AI in Oncology 167Priya Batta9.1 Introduction 1679.2 Literature Review 1709.3 Methodology 1729.4 Results 1749.5 Conclusion and Future Scope 174References 17510 Predictive Models for Cancer-Related Lymphedema: Enhancing Telerehabilitation and Physiotherapy Management 177Madhusmita Jena, Charu Chhabra, Huma Parveen, Sahar Zaidi, Noor Fatima and Habiba Sundus10.1 Introduction 17810.1.1 Prevalence of Lymphedema 17910.1.2 Diagnostic Technique for Lymphedema 18010.1.3 Commonly Used Scales for Diagnosis of Lymphedema 18110.2 Lymphedema’s Impact on Cancer Survivors 18110.3 Current Challenges in Lymphedema Management 18210.4 Role of AI in Lymphedema Management 18310.4.1 Customizing Physiotherapy Regimens Based on AI Predictions 18410.4.2 Integrating Telerehabilitation for Effective Lymphedema Management 18510.5 Conclusion 186References 18611 Role of AI in the Prediction of Leukemia and AI-Driven Predictive Models for Rehabilitation Outcomes in Acute Lymphoblastic Leukemia 189Huma Parveen, Charu Chhabra, Sahar Zaidi, Noor Fatima, Madhusmita Jena and Amaan Ali Khan11.1 Acute Lymphoblastic Leukemia 19011.2 Importance of Early Prediction and Rehabilitation in ALL 19111.3 Role of AI in Healthcare 19311.4 AI in Leukemia Prediction 19411.5 AI-Driven Predictive Rehabilitation Outcomes in ALL 19611.6 Data Privacy and Security in Healthcare Models 19911.7 Framework for Protecting Data Privacy 20011.7.1 Acts and Policies 20011.7.2 National Policies 20011.7.3 AI Models-Based Privacy Protection 20111.8 Ethical Concerns in AI Healthcare 202References 20312 Data Privacy and Ethical Challenges in AI-Driven Cancer Care 207Firdaus Jawed, Rabia Aziz, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan12.1 Introduction to Data Privacy and Ethics in AI-Driven Cancer Care 20812.2 Types of Sensitive Data in AI-Driven Cancer Care 20912.3 Ethical Frameworks and Guidelines for Data Privacy 21212.4 Data Security and Protection Techniques 21412.5 Bias, Fairness, and Algorithmic Transparency in AI-Driven Cancer Care 21612.6 Regulatory and Compliance Challenges 21912.7 Emerging Technologies and Innovations in Privacy 22112.8 Future Directions in Ethical AI for Cancer Care 22212.9 Conclusions 224References 22413 Cancer Rehabilitation in the Era of Targeted Chemotherapy and Personalized Immunotherapy 229Rabia Aziz, Firdaus Jawed, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan13.1 Evolving Landscape of Cancer Treatment 23013.2 Importance of Cancer Rehabilitation 23113.3 Integrating Rehabilitation Into AI-Powered Cancer Rehabilitation 23213.3.1 The Role of Data in Rehabilitation 23813.3.2 Machine Learning and Predictive Analytics 23913.3.3 Real-Time Monitoring and Feedback 23913.3.4 Outcomes Measurement and Continuous Improvement 24013.3.5 The Rationale for Integration 24113.3.6 Utilizing Biomarkers in Rehabilitation 24113.3.7 Multidisciplinary Collaboration 24213.3.8 Early Intervention Strategies 24213.3.9 Leveraging Technology for Monitoring and Feedback 24313.4 Leveraging Data Analytics and AI for Adaptive Rehabilitation 24313.4.1 The Role of Data Analytics in Rehabilitation 24413.4.2 AI-Driven Personalization of Rehabilitation Programs 24413.4.3 Integration of Wearable Technology and Telehealth 24513.4.4 Virtual Reality (VR) and Augmented Reality (AR) Applications 24513.5 Tailoring Rehabilitation Strategies for Targeted Therapies 24613.5.1 Understanding Targeted Therapies and Their Implications 24613.5.2 Personalized Assessment and Planning 24713.5.3 Integrating Evidence-Based Interventions 24713.5.3.1 Physical Therapy 24713.5.3.2 Occupational Therapy 24813.5.3.3 Psychosocial Support 24913.5.3.4 Nutritional Counseling 24913.5.4 Utilizing Technology for Enhanced Rehabilitation 25013.6 Future Directions and Emerging Trends 25113.7 Summary 251References 25214 Role of AI in Cancer Screening and Its Detection 257Muskan, Shweta Sharma, Parul Sharma, Manoj Malik and Jaspreet Kaur14.1 Introduction 25814.2 Cancer Mechanisms and Various Pathologies 25814.3 Conventional Methods of Cancer Screening 26014.3.1 Mammography 26014.3.2 Ultrasound 26214.3.3 Magnetic Resonance Imaging 26214.3.4 Liquid Biopsies 26214.3.5 Pap Smear (Papanicolaou Test) 26314.3.6 Barium X-Ray (Barium Swallow or Enema) 26314.3.7 Photoacoustic Tomography (PAT) 26314.3.8 SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography) 26314.4 Overview of AI (Artificial Intelligence) in Cancer Detection 26414.5 AI Applications in Cancer Screening Using Deep Learning and Machine Learning 26614.5.1 AI Models for Breast Cancer 26614.5.2 AI Models for Lung Cancer 26614.5.3 AI Models for Skin Cancer 26714.5.4 AI Models for Gastric Cancers 26714.5.5 AI Models for Prostate Cancers 26914.6 Challenges in AI Adoption for Cancer Screening 27014.7 Proposed Strategies for AI Implementation for Cancer Detection 27114.8 Conclusion 27214.9 Future Directions 273References 27415 Automated 3D U-Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI-Driven Cancer Research Applications 279S. Usharani, P. Manju Bala, T. Ananth kumar and G. Glorindal Selvam15.1 Introduction 28015.2 Literature Review 28315.2.1 Brain Tumor MRI Image Segmentation 28315.2.1.1 Methods for Manual Segmentation 28315.2.1.2 Methods for Partly-Automated Segmentation 28315.2.1.3 Methods for Absolutely Automated Segmentation 28415.2.2 Brain Tumor MRI Classification 28515.3 Materials and Methods 29015.3.1 Materials 29015.3.2 Methods 29115.3.2.1 System Model 29115.3.2.2 Multi Scale Feature Extraction Network 29315.3.2.3 Incremental Feature Improvement 29415.3.2.4 Loss Function 29515.4 Experimental Setup 29615.4.1 Experimental Analysis 29615.5 Conclusion 301References 30216 Early Prediction of Bone Cancer: Integrating Deep Learning Models 309R. Dhinesh, T. Ananth kumar, P. Kanimozhi and Sunday Adeola Ajagbe16.1 Introduction 31016.2 Related Works 31116.3 Proposed Methodology 31316.4 Results and Discussion 31816.5 Conclusion 321References 32217 Machine Learning Techniques for Predicting Epileptic Seizures: A Data-Driven Analysis Using EEG Signals 325Preeti Narooka, Ankit Vishnoi and Jatin Verma17.1 Introduction 32617.1.1 Background 32617.1.2 Objective 32617.2 Literature Survey 32717.2.1 Study 1: Feature Extraction Techniques in EEG-Based Seizure Detection 32717.2.2 Study 2: Application of Deep Learning in Neurological Disorders 32717.2.3 Study 3: Comparative Analysis of ML Algorithms 32817.2.4 Study 4: Transfer Learning in EEG Analysis 32817.2.5 Study 5: Real-Time Seizure Prediction Systems 32817.2.6 Study 6: Explainable Artificial Intelligence in Seizure Detection 32817.2.7 Study 7: Challenges in EEG-Based Seizure Detection 32917.2.8 Study 8: Multimodal Learning Approaches 32917.3 Methodology 32917.3.1 Dataset 32917.3.2 Preprocessing 33017.3.3 Feature Extraction 33017.3.4 Model Architecture 33117.4 Results and Discussion 33217.4.1 Model Performance 33217.4.2 Discussion 33317.4.3 Implications for Healthcare Applications 33417.5 Conclusion 334References 33418 Transfer Learning in Cancer Research 337Mamta and Nitin18.1 Definition and Overview of Transfer Learning 33818.1.1 Transfer Learning Typically Involves the Following Components 33818.1.2 Importance of Transfer Learning in Cancer Research 33918.1.3 Challenges in Traditional Cancer Research Approaches 34118.2 How Transfer Learning Works 34418.2.1 Types of Transfer Learning 34518.2.2 Transductive Transfer Learning 34618.3 Applications of Transfer Learning in Cancer Research 34618.4 Challenges in Transfer Learning for Cancer 34818.4.1 Data Scarcity and Domain Adaptation 34818.4.2 Model Interpretability 34918.5 Future Directions: Personalized Medicine and Drug Discovery 35118.5.1 Personalized Medicine: Tailoring Treatment to the Individual 35218.6 Drug Discovery: Accelerating the Path to New Therapies 35318.7 Challenges and Ethical Considerations 35418.8 Conclusion 354References 35519 Machine Learning Approaches for Early Detection of Cervical Cancer: A Comparative Study of Classification Models 359Inam Ul Haq, Janvi Malhotra, Vanshika Rawat, Jyoti Kumari and Gagandeep Kaur19.1 Introduction 36019.2 Literature Review of Some Research Papers 36419.3 Methodology 36819.4 Results 36919.5 Conclusion and Future Scope 370References 37020 Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data 375M. Rohini, S. Oswalt Manoj, J. P. Ananth and D. Surendran20.1 Introduction 37620.1.1 Need of Electronic Health Record Maintenance 37620.1.2 Message Passing Protocol for Cancer EHR Updates 37720.1.3 Reliable Messaging for Critical Data 37920.1.4 Microservice-Oriented Cancer Data Staging and Deployment 38020.2 HER Data Processing 38220.2.1 Staging Service 38220.2.1.1 Autoscaling Based on Criticality of EHR System 38220.2.2 Internal Working of the Staging Service 38320.2.2.1 Validate and Fetch Dashboard Details 38320.2.2.2 Execute Stored Procedure 38520.2.2.3 High-Availability Deployment Phase 38620.3 Conclusion 388References 38921 Artificial Intelligence–Driven Personalized Cancer Treatment 391Gurwinder Singh, Sarthak Sharma and Aastha Anand21.1 Introduction: The Dawn of Artificial Intelligence–Powered Cancer Screening 39221.2 Role of AI in Cancer Screening 39521.3 Role of AI in Early Detection 39921.4 Case Studies and Real-World Implementation 40121.5 Benefits and Opportunities 40421.6 Conclusion 40621.7 Future Scope 407References 40822 Revolutionizing Breast Cancer Detection: Emerging Trends and Future Technologies 411Gurmeet Kaur Saini, Inderdeep Kaur and Kanwaldeep Kaur22.1 Overview 41222.2 Risk Assessment Types 41322.3 Risk Elements 41322.4 Risk Factors for Hormones and Reproduction 41322.5 Additional Risk Factors 41422.6 Risk of Breast Cancer Over Time 41422.6.1 Risk Assessment by Family History 41422.7 Models for Risk Estimate 41522.7.1 The Gail Model 41522.7.2 Claus–Mammary Carcinoma Risk Assessment Model 41622.7.3 The BRCAPRO Model 41822.7.4 Tools for Risk Calculation 41822.8 Clinical Breast Imaging Techniques 41822.8.1 Mammography 41822.8.2 Ultrasonic 42022.8.3 Magnetic Resonance Imaging 42022.9 Measurement Systems and Techniques for Microwave Breast Imaging 42122.9.1 Tomography Using Microwaves 42122.9.2 Microwave Imaging Using Radar Technology 42122.9.3 Breast Cancer Detection Using Biosensors 42222.9.4 Use of Thermography to Find Breast Cancer 42222.10 Discussion 42322.11 Present Developments and Prospects for Breast Cancer Screening Methods 42322.12 Conclusion 426References 42623 Future of Neurological Research: Leveraging Artificial Intelligence for Precision and Discovery 431Hemlata and Utsav Krishan Murari23.1 Introduction 43123.2 AI in Neuroimaging: A Revolution in Neurological Research 43523.3 Computational Neuroscience and Modeling: Transforming Understanding of Neural Mechanisms through AI 43823.4 AI and BCIs: Transforming Accessibility and Real-Time Neural Interaction 44123.5 Ethics in the Integration of AI Into Neurological Research 44523.6 Conclusion 448References 44924 Cervical Cancer Detection Using Machine Learning 451Saranya. A., S. Ravi, Harsha Latha. P., T. Kalaichelvi and A. Anbarasi24.1 Introduction 45224.1.1 Overview of Medical Image Analysis 45424.2 ml Techniques for Cervical Cancer Diagnosis 45924.2.1 ml Algorithms 45924.2.2 Methodology of ML Classification of Images 46024.2.3 Cervical Cancer Image Dataset 46224.3 Related Work 46224.3.1 Cervical Cancer Detection Using ml 46324.4 Findings 46624.5 Performance Metrics in ml 47024.6 Conclusion 471References 47225 Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer 477Saranya. A., S. Ravi, Harsha Latha. P. and T. Kalaichelvi25.1 Introduction 47825.2 Motivation of Computer-Aided Diagnosis 48025.3 History of DL in Medical Imaging 48225.4 Deep Learning Application of Cervical Cancer 48225.5 Cervical Cancer Detection Based on DL Techniques for Medical Image Segmentation 48325.5.1 Deep Learning in Image Segmentation 48325.5.2 Deep Learning in Classification Task 48725.6 Frameworks Used in Detecting Cervical Cancer 48825.6.1 Comparison Between DL Segmentation and Classification 48925.7 Performance Metrics 49425.8 Conclusion 495References 495Index 499