A Comprehensive Review and Taxonomy on Machine Learning and Deep Learning Approaches for Brain Tumor Classification

Abstract

Cancer happens to be one of the most lethal diseases, resulting in high mortality rates. An unprecedented global surge in cancer cases can be seen, which leads to the necessity of developing newer methods for cancer detection so as to detect and arrest the progression of the disease quickly. Out of all cancer types, brain cancer happens to have a relatively higher mortality rate. Moreover, the brain being an internal organ makes invasive examinations challenging, thereby relying more on image-based prognosis. With an increased number of cases and extensive pressure on the current medical infrastructure, machine learning and deep learning (ML & DL)-based approaches are being explored extensively to automate some of the procedures so as to aid the medical practitioners by providing useful insights from medical data. In context to brain cancers, one of the foremost steps is identifying brain tumours, segmenting them and potentially classifying them as benign or malignant. The divergences in texture and sites of the tumours make this process complex and challenging to yield high accuracy. This paper presents a comprehensive review of the various contemporary image pre-processing, feature extraction and classification techniques employed in current literature to lay a foundation for future research in the domain with the objective of attaining high classification accuracy.

DOI: https://doi.org/10.24321/3117.4787.202601