Automated Segmentation and Classification of Brain Magnetic Resonance Images for Brain Tumor Diagnosis Based on Deep Learning

Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) are critical for effective diagnosis and treatment planning. This paper proposes a novel framework for brain tumor segmentation and classification using deep learning. The segmentation model is based on a modified U-Net architecture, called Residual Feature Pyramids U-Net with Attention (RFAU-Net), which incorporates residual blocks to enhance training depth, attention mechanisms to focus on relevant features, and a feature pyramid module to improve segmentation of small and complex tumor regions. To address class imbalance and pixel degradation during training, we introduce a combined loss function (CL) that integrates Weighted Focal Loss (WFL), assigning higher weights to minority classes and reducing the influence of majority classes. The model is evaluated on two publicly available datasets, achieving state-of-the-art performance with a segmentation accuracy of 97%, a Dice Similarity Coefficient (DSC) of 92.5%, and an Intersection over Union (IoU) of 92%. For tumor classification, we employ a Multi-Headed Convolutional Neural Network (MHCNN), achieving 99.8% accuracy in classifying the MGMT methylation status. These results demonstrate the superiority of the RFAU-Net model over traditional U-Net and RESU-Net architectures, particularly in handling small tumor regions and class imbalance. Additionally, a user-friendly web API is developed to classify brain tumors into MGMT methylated and unmethylated categories, enabling efficient integration of this model into clinical practice for improved diagnosis and treatment of gliomas.