Study on Machine Learning and Deep Learning in Medical Imaging Emphasizes MRI: A Systematic Literature Review

Due to the fast growth of medical imaging technologies over the last decade, medical practitioners and radiologists find it increasingly difficult to analyze and categorize medical images. Diagnosing, surgery planning, education, and inquiry benefit immensely from the abundance of information in medical images. The objective of our study was to use Machine learning (ML), and deep learning approaches have been applied for medical image analysis; this study focuses on ML for MRI evaluation (MRI). We provide a brief overview of the advances in medical image processing and image analysis utilizing machine and deep learning, and a few related issues. This study paper is limited to two digital databases: (1) Science Direct and (2) Google Scholar. This research report reviewed and discussed research publications. Our findings are based on a systematic literature review in which thematic analysis is done, and based on themes, we extract a comprehensive literature review on various issues, including image localization, segmentation, detection, and classification. DL approaches to analyzing brain MRI data have been extensively studied by performing a systematic review. Deep learning (DL)and machine learning techniques based on convolutional neural networks outperform traditional medical image classification, identification, and segmentation methods.