A Deep Learning Based Glioma Tumour Detection Using Efficient Visual Geometry Group Convolutional Neural Networks Architecture

Abstract The detection and segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) plays important aspects for last two decades. In this paper, Efficient Visual Geometry Group Convolutional Neural Networks Architecture is proposed for brain tumor detection which consists of pre-processing module, DWT module, and Classification module along with segmentation. The scaling based data augmentation methods are used as the pre-processing technique and the data augmented scaled images are decomposed using DWT Transform. The Wavelet feature maps are constructed from these decomposed coefficients and they are classified using EVGG-CNN architecture. This proposed architecture identifies the brain image into either Glioma or healthy brain image. The effectiveness of these developed methods stated in this work is verified by applying the proposed method on the brain images from BRATS 2021 and Kaggle dataset. The performance of proposed EVGG-CNN architecture is analyzed in terms of sensitivity, specificity and accuracy.