BRAIN TUMOR DETECTION AND CLASSIFICATION BY MRI IMAGES USING DEEP LEARNING TECHNIQUES

Abstract

MRI images [8] play a significant influence in brain tumor classification and detection but instead of having detection and classification using the medical equipment which is a radiologists or clinical professionals do a time-consuming and laborious task where accuracy depends only on the experience only, it can be beneficial to detect and classify the brain tumor by deep learning techniques and algorithms. As a result, using computer-assisted technologies to circumvent these limits is becoming increasingly vital. In this paper, the early detected and diagnosed brain tumor images along with their csv data has been used to find out the accuracy of the CNN algorithm for tumor detection and SVM algorithm for tumor classification into benign and malignant. HOG has been used for the feature extraction. After performing the experiment, it was observed that CNN achieved the detection accuracy of 87.02% and further tumor classification by employing SVM, the highest accuracy achieved was 96.35%. The experiment proved a very good accuracy of detection and classification even after using three different methods in the whole procedure.