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Manicka Senthamarai N is an Assistant Professor in the Department of Computer Science and Engineering at Velalar College of Engineering and Technology, Thindal, Erode, India. Her academic role involves teaching and academic activities in computer science and engineering. Her contribution focuses on deep learning techniques for medical image analysis and diagnosis.
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.