Abstract
In the area of pattern recognition and machine learning, features play a key role in prediction. The famous applications of features are medical imaging, image classification, and name a few more. With the exponential growth of information investments in medical data repositories and health service provision, medical institutions are collecting large volumes of data. These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other hand, this growth also made it difficult to comprehend and utilize data for various purposes. The results of imaging data can become biased because of extraneous features present in larger datasets. Feature selection gives a chance to decrease the number of components in such large datasets. Through selection techniques, ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision. The correct decision to find a good attribute produces a precise grouping model, which enhances learning pace and forecast control. This paper presents a review of feature selection techniques and attributes selection measures for medical imaging. This review is meant to describe feature selection techniques in a medical domain with their pros and cons and to signify its application in imaging data and data mining algorithms. The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data. Moreover, this review provides the importance of feature selection for correct classification of medical infections. In the end, critical analysis and future directions are provided.