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Ms. R. Karthika is an Assistant Professor in the Department of Information Technology at Rathinam Technical Campus, Coimbatore, Tamil Nadu, India. Her academic interests include Artificial Intelligence, Deep Learning, medical image analysis, computer vision, and intelligent information systems. She actively contributes to teaching and research in emerging technologies, focusing on AI-driven healthcare applications and advanced computational techniques.
Medical imaging is essential in contemporary healthcare since it facilitates early disease detection, therapy strategising, and patient surveillance. Imaging techniques including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Positron Emission Tomography (PET), fundus imaging, and histopathological imaging produce extensive clinical data necessitating precise analysis. Traditional image analysis relies heavily on radiologists and medical experts, resulting in protracted diagnostic processes and vulnerability to discrepancies among observers. The amalgamation of Artificial Intelligence (AI), Deep Learning (DL), Computer Vision, Internet of Medical Things (IoMT), Cloud Computing, and Explainable Artificial Intelligence (XAI) has markedly enhanced automated disease identification and categorisation. Deep learning frameworks like Convolutional Neural Networks (CNN), Residual Networks (ResNet), DenseNet, U-Net, Vision Transformers (ViT), Long Short-Term Memory (LSTM), and Hybrid Deep Learning Models have exhibited exceptional efficacy in identifying cancers, cardiovascular ailments, neurological conditions, retinal disorders, pulmonary infections, and musculoskeletal irregularities. This chapter elucidates the principles, framework, deep learning methodologies, applications, obstacles, and prospective research avenues pertaining to illness detection and classification in medical imaging through deep learning.