Modified CNN Architecture for Efficient Classification of Glioma Brain Tumour

Abstract

Magnetic Resonance Imaging (MRI) is the medical imaging modality that provides more useful functional data for the diagnosis of pathological conditions in brain tumour than any other modality. Manual observation of MRI data to diagnose the tumour is time-consuming and hence the objective of this work is to classify the Glioma brain tumour using a Convolutional Neural Network (CNN). This proposed work aims to design a new model of the modified CNN architecture for the classification of Gliomas. Various processes were used for the classification of MRI brain tumours, which include image pre-processing, image feature extraction, and subsequent classification of Glioma brain tumours. The proposed modified CNN obtained high classification accuracy of 94.65% compared to the pre-trained AlexNet Model. The traditional machine learning techniques like Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) gain an accuracy of 86.1% and 66.7%, respectively.