A Comprehensive Review on Strategies to Detect, Diagnose and Classify Brain Tumors

Abstract

Brain tumor is one of the most prevalent and life-threatening illness these days. A tumor is an aberrant mass of tissue caused by unrestrained cell proliferation and multiplication. It is important to detect and diagnose brain tumors at the early stages. For disease diagnosis at an initial stage, there is a high demand for accurate analysis of healthcare data. But tumors vary greatly in size, shape, and existence, making it extremely difficult to collect precise measurements in order to properly diagnose them. Digital image processing enacts a crucial role in the analysis of medical images for timely and efficient planning of treatment. This paper provides an insight into brain tumors, the mechanism involved in their detection along with the different image processing steps that can be applied to medical images for automating the brain tumor detection process. This paper reviews a significant number of recently proposed brain tumor detection techniques related to the current study along with their tabulated comparison. This work can help in designing a solution that provides different applications such as detection, localization, or identifying the type of tumor under a single model.