Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

Abstract

Digital image processing is a rising field for the investigation of complicated diseases such as brain tumor, breast cancer, kidney stones, lung cancer, ovarian cancer, and cervix cancer and so on. The recognition of the brain tumor is considered to be a very critical task. A number of approaches are used for the scanning of a particular body part like CT scan, X-rays, and Magnetic Resonance Image (MRI). These pictures are then examined by the surgeons for the removal of the problem. The main objective of examining these MRI images (mainly) is to extract the meaningful information with high accuracy. Machine Learning and Deep Learning algorithms are mainly used for analysing the medical images which can identify, localize and classify the brain tumor into sub categories, according to which the diagnosis would be done by the professionals. In this paper, we have discussed the different techniques that are used for tumor pre-processing, segmentation, localization, extraction of features and classification and summarize more than 30 contributions to this field. Also, we discussed the existing state-of-the-art, literature gaps, open challenges and future scope in this area.

A deep learning approach for brain tumor detection using magnetic resonance imaging

“…Radiologists can make a more accurate prognosis and increase the odds of long-term survival if a brain tumor is discovered early [10]. The tumor's shifting appearance, position, form, and size [16] remains a complex process. There has already been a lot of work done to assist doctors, patients, and researchers.…”