An investigation into deep learning for the analysis of medical images

Abstract

In recent years, deep learning has emerged as a pivotal paradigm in the analysis of medical images, with convolutional networks serving as a cornerstone of this advancement. This paper delves into a comprehensive exploration of the fundamental principles underpinning deep learning and its applications within the domain of medical image analysis. Through a meticulous review of many contemporary contributions, this study synthesizes the latest developments in the field, emphasizing tasks like image classification, object detection, segmentation, and registration. The inquiry spans diverse medical disciplines, encompassing neurology, retinal imaging, pulmonary studies, digital pathology, breast and cardiac evaluations, and musculoskeletal analyses. As a culmination, the paper not only assesses the present state-of-the-art achievements but also critically discusses persistent challenges and illuminates promising avenues for future research endeavors.