Current Applications of Deep Learning in Cancer Diagnostics

Häftad, Engelska, 2024

737 kr

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Beskrivning

This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.

Produktinformation

Utgivningsdatum:2024-10-09
Mått:156 x 234 x 10 mm
Vikt:340 g
Format:Häftad
Språk:Engelska
Antal sidor:167
Förlag:Taylor & Francis Ltd
ISBN:9781032223193

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Spelutveckling och spelprogrammering inom Data och IT
Databaser inom Data och IT

Mer om författaren

Jyotismita Chaki, PhD, is an Associate Professor at School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.Aysegul Ucar, PhD, is a Professor in Department of Mechatronics Engineering, Firat University, Turkey.

Innehållsförteckning

1. Contemporary Trends in the Early Detection and Diagnosis of Human Cancers Using Deep Learning Techniques, 2. Cancer Data Pre-Processing Techniques, 3. A Survey on Deep Learning Techniques for Breast, Leukemia and Cervical Cancer Prediction, 4. An Optimized Deep Learning Technique for Detecting Lung Cancer from CT Images, 5. Brain Tumor Segmentation Utilizing MRI Multimodal Images with Deep Learning, 6. Detection and Classification of Brain Tumors Using Light-Weight Convolutional Neural Network, 7. Parallel Dense Skip Connected CNN Approach for Brain Tumor Classification, 8. Liver Tumor Segmentation Using Deep Learning Neural Networks, 9. Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia, 10. Cervical Pap Smear Screening and Cancer Detection Using Deep Neural Network, 11. Cancer Detection Using Deep Neural Network: Differentiation of Squamous Carcinoma Cells in Oral Pathology, 12. Challenges and Future Scopes in Current Applications of Deep Learning in Human Cancer Diagnostics