Machine Learning in Medical Image Processing

Abstract

AbstractMedical images provide information that can be used to detect and diagnose a variety of diseases and abnormalities. Because cardiovascular disorders are the primary cause of death and cancer is the second, good early identification can aid in the reduction of cancer mortality rates. There are different medical imaging modalities that the radiologists use in order to study the organ or tissue structure. The significance of each imaging modality is changing depending on the medical field. The goal of this research is to give a review that shows new machine learning applications for medical image processing and gives a review of the field’s progress. The classification of medical photographs of various sections of the human body is the focus of this review. Additional information on methodology developed using various machine learning algorithms to aid in the classification of tumors, non-tumors, and other dense masses is available. It begins with an introduction of several medical imaging modalities, followed by a discussion of various machine learning algorithms to segmentation and feature extraction.