Medical Image Processing and Pattern Recognition: Adaptive Intelligence and Continuous Learning for Healthcare 5.0 presents a comprehensive and modern treatment of medical image analysis, integrating classical image processing foundations with advanced artificial intelligence methodologies. The book systematically guides readers from fundamental medical imaging modalities and image formation models to intelligent enhancement, segmentation, feature extraction, and deep pattern recognition techniques.
Distinctively positioned within the Healthcare 5.0 paradigm, this work emphasizes human-centric, explainable, and trustworthy AI systems for clinical imaging. Topics such as attention mechanisms, transformer architectures, explainable artificial intelligence, and cyber-physical healthcare systems are discussed in depth, highlighting their role in transparent and adaptive diagnostic workflows. Mathematical rigor is balanced with intuitive explanations, supported by illustrative figures, tables, and architectural diagrams.
Designed for postgraduate students, doctoral researchers, faculty members, and professionals in medical imaging, biomedical engineering, and healthcare AI, this book serves as both a structured academic textbook and a valuable research reference for intelligent and ethical medical imaging systems.