A Review on Disease Detection from Medical Images using Machine Learning

As a result of developments in imaging and processing, artificial intelligence may be used to perform a variety of radiological imaging tasks, including risk assessment, detection, diagnosis, prognosis, and therapy response, as well as multi-omics illness discovery. After reading this succinct overview of the topic, the reader will be able to recognize the nomenclature, numerous subfields, and components of machine learning, as well as its therapeutic potential. Radiomics is described as the process of transforming pictures into usable data. It is a subset of computeraided diagnostics. Quantitative radiomics' ultimate purpose is to either give projected image-based illness phenotypes for precision medicine or to provide quantitative image-based phenotypes for data mining in concert with other-omics for discovery (i.e., imaging genomics). Due to the complexity of deep networks, the constant improvement of computer software and hardware, and the difficulty of detecting minute changes in disease states versus variations in common things, well-annotated enormous data sets are required for deep learning to succeed in radiology. With imaging exams performed often in clinical practice, machine learning in radiology is predicted to have a significant clinical influence in the future, offering improved decision support during medical image interpretation. The term “decision support” is crucial since it implies that computers will assist people in making better decisions. Radiologists may be able to use computers in their everyday work to help them work more effectively with colleagues from other medical fields, which could lead to better precision medicine.