Skip to main content
Elsevier
View Cart

GANs in Medical Diagnostics and Classification

  • 1st Edition - October 1, 2026
  • Latest edition
  • Editors: Abhishek Kumar, Pramod Singh Rathore
  • Language: English

GANs in Medical Diagnostics and Classification explores the role of Generative Adversarial Networks (GANs) in addressing underdiagnosed and overlooked health challenges. The book b… Read more

World Book Day celebration

Where learning shapes lives

Up to 25% off trusted resources that support research, study, and discovery.

Explore resources

Description

GANs in Medical Diagnostics and Classification explores the role of Generative Adversarial Networks (GANs) in addressing underdiagnosed and overlooked health challenges. The book bridges cutting-edge AI research with real-world medical applications, presenting interdisciplinary insights into how GANs enhance diagnostics, treatment precision, and disease surveillance across domains such as antimicrobial resistance, rare genetic disorders, and environmental health risks. It also examines ethical, policy, and accessibility dimensions of AI in healthcare. By combining technical depth with practical case studies, this volume equips medical researchers, AI engineers, and healthcare professionals with actionable knowledge to tackle silent epidemics using GAN-based tools.

Key features

  • Delivers real-world case studies that demonstrate GAN applications in diagnosing rare diseases, detecting antimicrobial resistance, and enhancing medical imaging
  • Integrates interdisciplinary perspectives from medicine, AI, ethics, and policy to provide a holistic view of AI-driven healthcare innovation
  • Explores emerging technologies such as synthetic data generation, wearable diagnostics, and AI-enhanced telemedicine for future-ready healthcare solutions

Readership

Medical researchers, academic scholars in biomedical informatics, digital health, and AI in healthcare; healthcare professionals; data scientists and AI engineers working on healthcare applications

Table of contents

Section I: Silent Threats in Global Health

1. Antimicrobial Resistance: GAN-Driven Analysis and Classification

2. Rare Genetic Disorders: GAN-Based Diagnostic and Treatment Classification

3. Neglected Tropical Diseases: GAN AI Applications in Disease Surveillance

4. Microplastics and Health: GAN-Enabled Detection and Impact Assessment

Section II: Lifestyle and Environmental Influences

5. Digital Eye Strain: AI-Powered Classification of Risk Levels

6. Sedentary Lifestyle: GAN-Based Predictive Health Models

7. Air Pollution: GAN AI Classification of Chronic Disease Risks

8. Vitamin D Deficiency: AI-Driven Health Implication Assessments

Section III: Innovative Diagnostic Approaches

9. GAN-Based AI in Early Disease Detection

10. AI and GANs in Point-of-Care Testing: Transforming Rural Health

11. Wearable Technology and GAN-Powered Continuous Health Monitoring

12. Molecular Diagnostics Enhanced by GAN AI: Precision Medicine in Practice

Section IV: Therapeutic Innovations

13. Gene Therapy Guided by GAN-Based Predictive Models

14. Personalized Immunotherapy with GAN AI in Cancer Treatment

15. Nanomedicine Innovations Guided by GAN AI

16. Telemedicine Enhanced by GAN AI: Bridging Healthcare Gaps Globally

Section V: Social, Ethical, and Policy Perspectives

17. Medical Misinformation: GAN AI-Based Detection and Solutions

18. Ethical Dilemmas in GAN AI Medical Technologies

19. Healthcare Accessibility and Equity: GAN AI Applications

20. Policy Frameworks for GAN AI Integration in Addressing Silent Epidemics

Product details

  • Edition: 1
  • Latest edition
  • Published: October 1, 2026
  • Language: English

About the editors

AK

Abhishek Kumar

Abhishek Kumar is Assistant Director and Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He holds a Ph.D. in Computer Science from the University of Madras and is currently a Post-Doctoral Fellow with the Ingenium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He received his M.Tech in Computer Science and Engineering and B.Tech in Information Technology from Rajasthan Technical University, Kota, India. He has over thirteen years of academic teaching experience. His research interests include artificial intelligence, computer vision, image processing, data mining, machine learning, and renewable energy systems. He has authored and edited several books with leading international publishers and serves as a reviewer for reputed journals.

Affiliations and expertise
Chandigarh University, Punjab, India

PS

Pramod Singh Rathore

Dr. Pramod Singh Rathore is currently an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur, in India. He completed his PhD in computer science and engineering at the University of Engineering and Management (UEM), Jaipur, India. With over 12 years of academic teaching experience, he has more than 85 publications in peer-reviewed national and international journals, books, and conferences. He has co-authored or co-edited numerous books with well-known publishers. Dr. Singh Rathore’s research interests include NS2, computer networks, mining, and DBMS. He serves on the editorial and advisory committees of the Global Journal Group and is also a member of various national and international professional societies in the fields of engineering and research, including the ACM and International Association of Engineers (IAENG).

Affiliations and expertise
Department of Computer and Communication Engineering, Manipal University, Jaipur, India