Multi-class brain tumor MRI segmentation and classification using deep learning and machine learning approaches

BACKGROUND: Brain tumor classification using Magnetic Resonance Imaging (MRI) is crucial for diagnosis and treatment planning. The differentiation between malignant and benign brain tumors and their subtypes remains a challenging task that can benefit from advanced computational techniques. PURPOSE: This study uses an MRI dataset to explore the effectiveness of deep learning (DL) and machine learning (ML) approaches for classifying brain tumors. MATERIALS AND METHODS: A dataset comprising 1200 DICOM brain tumor MRI images, representing malignant and benign tumors with six subtypes, was prepared. Each image was converted to a 512 × 512-pixel digital format, selecting 200 images per tumor class. Image quality was enhanced using sharpening algorithms and mean filtering. The proposed edge refined binary histogram segmentation (ER-BHS) was applied to extract hybrid features from the regions of interest. Feature optimization through a correlation-based method reduced the dataset to 11 key features. Multiple classifiers, including DL, neural networks, and ML models, were evaluated on the optimized dataset using 10-fold cross-validation. RESULTS: Among the tested models, the random committee (RC) classifier demonstrated superior performance, achieving an accuracy of 98.61% on the optimized hybrid brain tumor MRI dataset. Overall, DL and ML methods effectively automated brain tumor classification. CONCLUSION: The promising results affirm the potential of DL and ML approaches to enhance medical image analysis and improve diagnostic accuracy in brain tumor classification, potentially revolutionizing clinical workflows.