A Comprehensive Method for MRI Brain Tumor Classification Using Hybrid CNN and Feature Selection

The classification of MRI brain tumors is crucial in medical image processing. However, current classification techniques are ineffective in differentiating benign from malignant tumors, which raises mortality rates. This article proposes a comprehensive methodology that includes data augmentation, a hybrid CNN model, an optimal feature selection method, and a set of classifiers to increase classification accuracy and decrease mortality rates. The proposed method has four steps. First, Lipschitz-based data augmentation is used to reduce data overfitting problems. Second, in order to improve feature extraction, a hybrid CNN model that is composed of three different CNN models, such as AlexNet, VGG-19, and ResNet-50, is utilized. Thirdly, to choose the best features, the Minimum Redundancy Maximum Relevance approach is used. Finally, for the classification task, a set of classifiers comprising SVM and KNN are used. The results obtained using the proposed method are superior to those obtained using the methods that are currently being utilized.