Analysis of Medical Image Processing using Machine Learning Applications - A Review

Intelligent health systems and a range of patient care can benefit from the help of artificial intelligence. In the medical field, artificial intelligence methods from machine learning to deep learning are widely used for patient risk assessment, medication development, and illness diagnosis. To accurately detect illnesses using artificial intelligence approaches, a variety of medical data sources are needed, including computed tomography scans, genomes, mammograms, ultrasound, magnetic resonance imaging, and more. Additionally, artificial intelligence mainly improved the experience of patients in the hospital and expedited the process of getting them ready to continue their recovery at home. This article discusses a thorough analysis of artificial intelligence-based methods for diagnosing a wide range of illnesses, including cancer, diabetes, Alzheimer's disease, chronic heart disease, stroke, cerebrovascular, hypertension, skin, and liver disease. We carried out a thorough analysis that included the medical imaging dataset that was utilized, as well as the feature extraction and classification procedure for making predictions. For the purpose of early prediction of various disease types using artificial intelligence-based methods, articles published up until October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information are chosen based on preferred reporting items for systematic reviews and Meta-Analysis guidelines.