K-Net-Deep joint segmentation with Taylor driving training optimization based deep learning for brain tumor classification using MRI

Abstract

ABSTRACT Globally, a huge number of people succumb to brain tumour, which is considered to be one of the lethal types of tumours. In this research, an effective brain tumour segmentation and classification approach is implemented using Deep Learning (DL) based on Magnetic Resonance Imaging (MRI). Here, the segmentation of the tumour region from the brain image using the proposed hybrid K-Net-Deep joint segmentation (Deep K-Net), wherein the segmentation results produced by K-Net and Deep joint segmentation are combined using the Ruzicka similarity. Further, a Driving Training Taylor (DTT) algorithm is presented for training the K-Net. Classification is accomplished using the Shepard Convolutional Neural Network (ShCNN) that is optimized with the help of the proposed DTT algorithm. Further, the efficiency of the DTT_ShCNN is examined based on , accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) with values of 0.936, 0.943, 0.945, and 0.949, respectively.