Häftad, Engelska, 2026

Lightweight, Real-time Deep Learning Models for Healthcare Applications

Av Tina Babu, Rekha R Nair, Balamurugan Balusamy

2081 kr

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Beskrivning

Lightweight, Real-time Deep Learning Models for Healthcare Applications addresses the pressing need for deploying efficient artificial intelligence models in clinical environments constrained by limited computational resources. As AI adoption accelerates in healthcare, this reference targets the gap between advanced deep learning research and practical real-time applications, emphasizing model optimization, privacy, and regulatory compliance. The content comprehensively covers model compression techniques such as pruning, quantization, knowledge distillation, and neural architecture search, alongside federated learning frameworks and real-time inference optimization. Detailed chapters present hardware-aware strategies, security considerations, continuous learning, and regulatory validation. The book also provides extensive case studies and implementation blueprints demonstrating practical deployment on devices like Raspberry Pi, NVIDIA Jetson, and mobile health platforms. This book benefits researchers, clinicians, and healthcare technology professionals by offering practical solutions to develop, validate, and implement AI models that maintain diagnostic accuracy while meeting clinical constraints. It equips students and educators with theoretical foundations and hands-on examples to foster innovation in biomedical AI, ultimately enhancing diagnostic capabilities and patient care in diverse healthcare settings.

Provides end-to-end case studies with deployment blueprints for healthcare AI applications
Explores advanced model compression and optimization techniques tailored for medical use
Includes compliance and ethical considerations for real-time AI in clinical environments
Delivers practical methodologies for deploying AI on resource-constrained medical devices
Covers federated learning and privacy-preserving AI for multi-institutional healthcare networks

Produktinformation

Utgivningsdatum: 2026-12-01
Mått: 191 x 235 x undefined mm
Vikt: 450 g
Format: Häftad
Språk: Engelska
Antal sidor: 300
Förlag: Elsevier Science
ISBN: 9780443454264

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Mer om författaren

Dr. Tina Babu is an accomplished engineering educator and researcher currently serving as Assistant Professor in the Department of Computer Science and Engineering at Alliance University, Bengaluru. With over 11 years of academic experience, she specializes in image processing, artificial intelligence, machine learning, and pattern recognition. Dr. Babu holds a Ph.D. focused on automated colon cancer screening and grading of histopathological images, demonstrating her expertise in applying AI to medical diagnostics. Her research contributions include over 50 publications in prestigious journals and conferences, with particular emphasis on medical image analysis and deep learning applications. She has also secured multiple patents and received research funding from SERB. Dr. Babu maintains an impressive academic profile with an h-index of 10. Her teaching portfolio encompasses advanced courses in Computer Vision, Machine Learning, and Database Management Systems, reflecting her commitment to nurturing the next generation of technology professionals. Dr. Rekha R. Nair is a seasoned academic and researcher with extensive experience in computer science, specializing in medical image processing, machine learning, data science, and artificial intelligence. She currently serves as an Assistant Professor in the Department of Computer Science and Engineering at Alliance University, Bengaluru. Previously, she was a faculty member at Dayananda Sagar University and completed her Ph.D. at Amrita Vishwa Vidyapeetham, where her research focused on developing algorithms for multi-modal medical image fusion. Over the years, Dr. Nair has taught a variety of computer science courses, such as Artificial Intelligence, Data Science, and Blockchain Technology, while actively contributing to curriculum development and department administration. Dr. Nair's research endeavors are well-recognized, with numerous publications in top-tier journals and conferences. Her work includes pioneering efforts in medical image fusion and cancer detection using deep learning frameworks. She has collaborated on projects addressing real-world problems, such as IoT-driven energy conservation and academic integrity using AI models. Notably, she has contributed to various book chapters and holds patents for innovative technological solutions. In addition to her scholarly achievements, Dr. Nair actively participates in faculty development programs and serves as a reviewer for several prestigious scientific journals, enhancing her contributions to the research community. Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences

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