A Hybrid Deep Learning Framework for MRI-Based Brain Tumor Classification Processing

Classifying tumors from MRI scans is a key medical imaging and diagnosis task. Conventional feature-based methods and traditional machine learning algorithms are used for tumor classification, which limits their performance and generalization. A hybrid framework is implemented for the classification of brain tumors using MRIs. The framework contains three basic components, i.e., Feature Extraction, Feature Fusion, and Classification. The feature extraction module uses a convolutional neural network (CNN) to automatically extract high-level features from MRI images. The high-level features are combined with clinical and demographic features through a feature fusion module for better discriminative power. The Support vector machine (SVM) was employed to classify the fused features as class label tumors by a classification module. The proposed model obtained 90.67% accuracy, 94.67% precision, 83.82% recall and 83.71% f1-score. Experimental results demonstrate the superiority of our framework over those existing solutions and obtain exceptional accuracy rates compared to all other frequently operated models. This hybrid deep learning framework has promising performance for efficient and reproducible tumor classification within brain MRI scans.