Visualizing Wellness: The Impact of Deep Learning on Healthcare Image Analysis

Deep convolution networks have quickly established itself as the main method for deciphering medical pictures due to their widespread use. This extensive study examined almost 300 recent papers, mostly from the preceding year, that summarized the basic artificial intelligence ideas related to medical picture analysis. Applications of deep learning were explored in detail in a number of disciplines, including object recognition, picture grouping, classification, and registering. This book presents a succinct summary of studies conducted in the fields of neurological disorders, retinal cells, asthma, malignant scans, breasts, cardiovascular and cardiac pelvic, and nerve imaging. Study conducted concludes our assessment with a review of the state-of-the-art at this time, a thorough investigation of outstanding problems, and suggestions for further research. All areas, including computerized pathology, musculoskeletal, pulmonary, the heart, breasts, muscles and tendons, retinal, and neurology imaging, are summarized in a summary of current studies. This thorough review ends with an assessment of the field's current state, a critical analysis of ongoing issues, and specific suggestions for further research and development.