Dr Srinivas Arukonda, Assistant Professor, Department of Computer Science and Engineering, India authored a book chapter titled “Explainable Lightweight Transformer-Based Neural Network for Multi-Label Medical Image Classification”, in Transformative Role of Transformer Models in Healthcare, IGI Global, 2025.

The chapter introduces a fast, efficient, and interpretable transformer-based model for detecting multiple diseases from medical images. Using ChestX-ray11 data, the proposed lightweight architecture outperforms existing methods while reducing computational cost.

The chapter also integrates Grad-CAM++ to clearly visualise disease-specific regions, improving clinical trust and transparency. This work contributes to the development of responsible, explainable, and deployable AI in healthcare, making advanced diagnostic support more accessible for real-world medical applications.

About the Book

Transformative Role of Transformer Models in Healthcare explores how transformer-based architectures—originally developed for natural language processing—are now revolutionizing medical and clinical domains. These models enable accurate diagnostics, personalized treatment, predictive analytics, and automation of healthcare processes by learning from massive volumes of medical data, including EHRs, medical literature, and genomic datasets. By bridging AI innovation and medical practice, the book highlights how transformers are shaping the future of healthcare through precision medicine, patient-centered care, and intelligent decision support systems.