Machine Learning-based Brain Tumor Segmentation: A Comprehensive ‎Review

Abstract

Segmentation of Brain tumors refers to a crucial function in medical image processing. Despite a lot of main attempts and satisfactory results in such a field, appropriate classification and segmentation remain an important function. Segmentation of an image is a hard task in the processing of an image. In order to improve the efficiency of processing, analysis, and detection, hospitals have already begun to use machine learning (ML). To increase the speed of the recovery process started, doctors could get help with detection. In the last few years, techniques of machine learning have illustrated satisfactory performance in solving different issues of computer vision like semantic segmentation, image classification as well as object diagnosis. Several ML-based methods have been successfully applied to the problem of brain tumor segmentation. The present paper shows an overview of recent ML and deep learning techniques to diagnose and group brain illnesses from MRI images. More than 60 scientific studies are chosen and discussed here, covering technical features like network architecture design, and segmentation.