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The book, “Applications of Convolutional Neural Networks for Automatic Disease Diagnosis”, is born out of a deep interest in the transformative potential of deep learning in the medical field. It aims to offer a comprehensive overview of how CNNs are being applied to identify and classify a wide range of human diseases, such as cardiovascular diseases, skin cancer and brain tumor. 
 
This book begin with foundational concepts and an understanding of basic CNN architecture. Next, an extensive literature survey is carried out about usage of various intelligent techniques in heart disease diagnosis. Further, we delve into real-world applications, highlighting the role of CNNs in diagnostic tasks across radiology, ECG, dermatology, and beyond. Case studies, current research trends, and emerging challenges are discussed in depth, bridging theory with practice. Throughout the chapters, we combine theoretical foundations with practical applications, using real-world case studies and datasets wherever possible. 
 
This book is intended for researchers, data scientists, medical professionals, and students who seek to understand both the promise and limitations of CNN-based diagnostic systems. It also hopes to inspire further innovation by shedding light on how data-driven approaches can support and augment human expertise in clinical settings. This book does not claim to replace the expertise of clinicians or the complexity of human judgment in medicine. Instead, it aspires to be a companion—highlighting how deep learning can augment diagnostic processes, reduce error rates, and ultimately contribute to better patient outcomes.