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Dr. D. Agalya is an Assistant Professor in the Department of Computer Science at the School of Arts and Science, Vinayaka Mission's Chennai Campus, Vinayaka Mission's Research Foundation, Paiyanoor, Chennai, India. Her academic background is in computer science. Her contribution to this volume explores deep learning applications in medical image analysis and diagnostic systems.
Deep learning has become a significant computational method for analysing medical images, facilitating automated feature extraction, picture categorisation, anatomical segmentation, lesion identification, and computer-aided diagnosis. This chapter delineates the theoretical underpinnings, architectural tenets, preprocessing techniques, training methodologies, evaluation criteria, and practical utilisations of deep learning in medical image analysis and diagnosis. It addresses categorisation, object recognition, semantic segmentation, anomaly identification, image reconstruction, and multimodal learning. Novel trajectories such as self-supervised learning, foundational models, federated learning, interpretable AI, and three-dimensional medical imaging analysis are examined. The section highlights the necessity for deep learning systems to get thorough validation and to be utilised as decision-support instruments with suitable clinical supervision. Prudent execution can enhance the efficacy and uniformity of image analysis while fostering the comprehensive advancement of intelligent and interconnected healthcare systems.