Navigating the Depths: A Comprehensive study of Deep Learning and its Advancements in Medical Image Analysis

Abstract

Medical imaging is the technique of capturing images of internal organs for diagnostic purposes, contributing to the identification and study of diseases. The primary goal of medical image analysis is to enhance the effectiveness of clinical research and treatment options. Deep learning has revolutionized this field, demonstrating remarkable outcomes in tasks such as registration, segmentation, feature extraction, and classification. The increased availability of computational resources and the resurgence of deep convolutional neural networks are key drivers for these advancements. Deep learning techniques excel in uncovering concealed patterns within images, aiding clinicians in achieving precise diagnoses. They have proven to be particularly effective in organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Numerous deep learning approaches have been proposed for various diagnostic purposes in the analysis of medical images. A significant hurdle in integrating deep learning models into the medical domain is the scarcity of training data, primarily attributed to the challenges associated with collecting and accurately labelling data, a task requiring expertise. To address this limitation, transfer learning (TL) has emerged as a valuable strategy, leveraging pre-trained state-of-the-art models to tackle various medical imaging tasks. This comprehensive review highlights the methodologies, including preprocessing, segmentation, feature extraction, and classification, and evaluates the performance of various DL models and also the recent advancements in the deep learning like transfer learning approaches in medical image processing.