A distinctive ensemble deep learning model for brain tumor MRI image classification

Abstract

Brain tumor detection is challenging for radiologists. The early detection of brain tumors is critical, and automated techniques are necessary to achieve this goal. In this study, an automated method is proposed to distinguish between malignant and non-cancerous brain Magnetic Resonance Images (MRI). An ensemble technique was proposed that includes two Deep Learning (DL) models, a 6-Convolutional Neural Network (6-CNN) model with an efficient end-to-end, and a pre-trained Residual Network50 (ResNet50) model. The MRI image classification was experimented in two directions: one using the average probabilities of the ensemble model, and the other considering the optimal weights of the ensemble model for a Support Vector Machine (SVM) classifier with different kernels. Two datasets, Harvard and Retrospective Image Registration Evaluation (RIDER), were used to evaluate the performance of the proposed model. The 6-CNN-ResNet-SVM model achieved the highest accuracy of 97.32% for 10-fold validation, with the remaining performance metrics being an Area Under the Curve (AUC) of 0.98%, sensitivity of 93.62%, specificity of 98%, False Negative Rate (FNR) of 0.06, and False Positive Rate (FPR) of 0.00 for the linear kernel. The proposed model can identify tumors more accurately and quickly than existing approaches.