A review on deep learning in medical image analysis

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.

Automated summary

Ongoing advances in AI, particularly in deep learning techniques, have been crucial in identifying, classifying, and quantifying patterns in clinical images. The rapid development of deep learning has found effective applications in various medical fields, including neurology, pathology, radiology, and more. This paper presents research on the application of deep learning in medical image processing, showcasing both fundamental information and cutting-edge approaches in this area.