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

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.

This study uses an MRI dataset to explore the effectiveness of deep learning (DL) and machine learning (ML) approaches for classifying brain tumors.

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.

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.

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.