Automatic Multi-Class Brain Tumor Classification Using Residual Network-152 Based Deep Convolutional Neural Network

Brain tumor is one of the leading causes of death in humans worldwide. Image recognition or computer vision uses deep learning based approaches for automatic tumor detection by classifying brain images. It is difficult to analyze the similarity between brain tissues while processing the magnetic resonance imaging (MRI) brain images for tumor classification. In this paper, residual network-152 (ResNet-152) with softmax layer is proposed for accurate detection of brain tumor with low complexity. Initially, the brain images are pre-processed and segmented with adaptive canny mayfly algorithm (ACMA). More discriminative features are extracted from the pre-processed image with spatial gray level dependence matrix (SGLDM), and optimal features are selected with modified chimpanzee optimization algorithm (MChOA). The optimal feature selection and optimal performance of classification are obtained by eliminating poor generalization and over specialization. After eliminating redundancies, the features are fed to residual classification. The overall performance of the proposed tumor classification method is evaluated using various parameters such as accuracy, precision, recall, F-score, MCC and balanced accuracy. The evaluation results indicate that our proposed method reached the accuracy level of 98.85%, which is efficient than other conventional approaches such as convolutional neural network (CNN), ResNet, recurrent neural network (RNN), random belief network (RBN), liner support vector machine (LSVM) and poly-SVM.