Skin Lesion Detection and Classification using Transfer Learning Models

This book presents a comprehensive study on skin lesion detection and classification using advanced transfer learning models, addressing one of the most critical challenges in modern healthcare early diagnosis of skin diseases. It explores how deep learning architectures, pre-trained on large datasets, can be effectively adapted to medical imaging for accurate and efficient lesion analysis. Covering key concepts such as image preprocessing, feature extraction, model selection, and performance evaluation, the book provides a practical and research-oriented approach. It highlights popular transfer learning models like CNN-based architectures and demonstrates their application in identifying various skin conditions, including melanoma. Designed for researchers, students, and professionals in artificial intelligence, biomedical engineering, and healthcare analytics, this book bridges the gap between theory and real-world implementation, offering insights into building reliable, intelligent diagnostic systems.