Medical imaging plays an important role in various clinical applications such as: B. Medical procedures used for early detection, monitoring, diagnosis, and therapeutic evaluation of various medical conditions. Understanding the principles and implementations of artificial neural networks and deep learning is essential to understanding medical image analysis in computer vision. Deep learning approaches (DLA) in medical image analysis are becoming a rapidly growing research area. DLA is widely used in medical imaging to indicate the presence or absence of disease. This paper describes the development of artificial neural networks, a comprehensive analysis of DLA that brings promising medical imaging applications.
Most DLA implementations focus on X-ray, computed tomography, mammography, and digital histopathology images. Provides a systematic review of articles on DLA-based classification, recognition, and segmentation of medical images. This review guides the investigator to consider appropriate modifications of her DLA-based medical image analysis.
