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Expected by: 01 January 2027
Deep Learning Models in Medical Imaging
Diagnostic innovations and clinical intelligence
by Rajakumar Girija, S.L. Jayalakshmi, R. Vedhapriyavadhana
As healthcare technology moves towards a near-human level of performance in image recognition for screening, diagnosis, staging, and prognosis, this authored book lays the groundwork for understanding AI's transformative impact on the analysis of medical images.
The book introduces fundamental AI concepts - machine learning and deep learning - and outlines how they are applied in diagnostic automation. Readers are introduced to common imaging techniques like X-ray, MRI, CT, and ultrasound, as well as their integration into clinical settings. Historical milestones, current technologies, and deployment challenges are discussed alongside the associated legal, ethical, and bias-related issues.
The book covers the complete pipeline from image acquisition to model deployment in clinical settings. Special focus is given to lightweight, explainable AI (XAI) systems suitable for resource-constrained healthcare environments.
The authors explore diagnostic applications such as diabetic retinopathy screening using fundus images, skin lesion classification, interpretation of X-ray images, mammography, and brain scans. Novel techniques including federated learning, self-supervised models, and domain adaptation for diagnostic robustness are thoroughly explored, highlighting the global significance of AI-driven healthcare.
Deep Learning Models in Medical Imaging: Diagnostic innovations and clinical intelligence is suitable for an audience of AI researchers in healthcare, biomedical engineers, diagnostic staff and others working in medical imaging and data science.
About the Author
R. Girija is an associate professor in the Department of Computer Science and Engineering and in charge of the Centre for Health Innovations (CHI) at Manav Rachna International Institute of Research and Studies (MRIIRS), India. Her research focuses on artificial intelligence, federated learning, computer vision, explainable AI, medical image analysis, industrial AI, optical image encryption, cryptography, and trustworthy intelligent systems. She has authored multiple SCI-indexed publications, holds six patents, and leads interdisciplinary research and innovation projects spanning healthcare, industrial automation, and intelligent digital technologies.
S.L. Jayalakshmi is an assistant professor in the Department of Computer Science at Pondicherry University (Main Campus), India. She has over 15 years of teaching experience and has presented her research at numerous national and international journals and conferences. Her research interests include machine learning, speech and audio signal modelling. She has published a patent, received a SEED grant to undertake a funded research project, and successfully completed two consultancy projects.
R. Vedhapriyavadhana is a lecturer at the School of Computing, Engineering and Physical Sciences, at the London campus of the University of the West of Scotland, UK and a visiting faculty at Wuxi Taihu University, China. She brings together 20 years of experience in industry, academia, and research specialising in digital image processing, AI, deep learning, computer vision, and cybersecurity. She holds two patents and has led funded projects under DST-India and industry consultancy.
Publication Year: 2026
Pages: 300
ISBN-13: 978-1-80705-076-4
Format: HBK