Application of Hybrid Classifier for Multi-class Classification of MRI Brain Tumor Images

Correctly classifying the brain tumor images is a matter of utmost importance for protecting the lives of brain cancer patients. The individual infliction of deep learning and machine learning algorithms has shown a diminutive impact on accurate brain tumor classification. This study goals to create a hybrid classifier based on deep learning (DL) and machine learning (ML) algorithms to differentiate MRI images of brain tumors into multiple categories such as meningioma, glioma, and pituitary. A simple convolutional neural network (CNN) based DL model consisting of ten layers is developed to extract precise features of tumors. Then various ML algorithms such as support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), gaussian naive bayes (G-NB), decision tree (DT), logistic regression (LR), and linear discrimination (LD) are utilized to enhance the accurate classification rate than normally used softmax classifier. The effectiveness of the modeled hybrid classifiers is evaluated based on four verification statistical metrics such as accuracy, precision, recall, and F1-score. The results exhibit that among the seven several hybrid classifiers, the CNN-KNN classifier shows an improved testing performance in terms of classifying brain tumor images with 99.45% accuracy, 99.40% accuracy, 99.34% recall, and 99.37% F1-score. Among seven different classifiers, the CNN-KNN classifier outperformed other existing state-of-art methods with an improved classification accuracy of 1.45%.