A Survey on Various Machine Learning Techniques for an Efficient Brain Tumor Detection from MRI Images

Abstract

On account of the uncontrolled and quick growth of cells, Brain Tumor (BT) occurs. It may bring about death if not treated at an early phase. Brain Tumor Detection (BTD) has turned out to be a propitious research field in the current decennia. Precise segmentation along with classification sustains to be a difficult task in spite of several important efforts and propitious results in this field. The main complexity of BTD emerges from the change in tumor location, shape, along with size. Providing detailed literature on BTD via Magnetic Resonance Imaging (MRI) utilizing Machine Learning (ML) methods to aid the researchers is the goal of this review. Diverse datasets are mentioned which are utilized most often in the surveyed articles as a prime source of Brain Disease (BD) data. Furthermore, a concise epitome of diverse segmentation methods that are utilized in diagnosing BDs has been offered. Lastly, an outline of key outcomes from the surveyed articles is exhibited, and several main problems related to ML-centred BD diagnostic methodologies are elucidated. The most precise method to detect diverse BDs can be engaged for future advancement via this study.