An analysis of medical images using deep learning

The use of AI models in health care system and the life sciences is expanding. In this paper, we will take a look at the present state of the art and address the unanswered issues regarding the development of Ai technologies as clinical decision support tools. A review, which included a critical examination of papers published from 1990 and 2022, led the study's most challenging aspects.First, we demonstrate the structural distinction between ML and DL methods. Methods for training, validating, and testing ML models, as well as feature extraction, are described. In DL, models are provided as multi-layered artificial neural networks for direct image analysis. Data management includes technical stages like as image labelling, picture annotation, data standardization, and federated learning. After that, we divide the following into their own subsections: sample size computation, including frequent trials in AI methods; data augmentation strategies for coping with limited or unequal data; and the understandability of AI models. Finally, the advantages and disadvantages of ML and DL in introducing AI applications to diagnostic imaging are compared and contrasted. Biomedical and healthcare systems rank high on the list of important topics for AI applications, with medical imaging ranking as one of the most relevant and promising fields in which to apply such technology. Gaining insight into the specific difficulties associated with developing and deploying such systems in healthcare situations is helpful.