Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Abstract

Brain tumors being ninth in terms of prevalence and one of the most frequently diagnosed malignant tumors, negatively impact millions of individuals. Identifying and classifying tumors from MRI used for health monitoring poses a challenge for radiologists, yet early detection could significantly enhance the chances of effective treatment. Researchers in the field of explainable AI are currently focused on developing sophisticated techniques to classify and diagnose brain diseases. This study presents a novel framework that enhances the interpretability of our proposed system for brain tumor detection by utilizing explainable AI techniques. To enhance interpretability, we integrate the optimized recursive feature elimination selection technique with support vector machines. This method effectively eliminates redundant features, identifies the most important ones, and enhances the efficiency of detecting tasks. Following that, the optimal recursive feature elimination (ORFE) features are combined using the supervised support vector machine (SVM) technique. While EfficientNet-CNN is a very useful feature extraction framework that extracts the most important features from a transparent model, we reduced the overall computational complexity through feature elimination and supervised models, and the Figshare dataset clearly demonstrated the efficacy of our model. This study achieved very exceptional results and reduced computational complexity as compared to a single CNN model. The experimental results indicate that the proposed SVM-RFE based technique accurately detects brain tumors with a 99.51% accuracy and a specificity score of 99.63\%. The proposed approach obtained an accuracy of 98.93% with a standard deviation of 0.032 using 10-fold cross-validation. Additionally, it produced an optimal ROC_AUC of 100% for cases including meningiomas and pituitary tumors.