Brain Tumor Segmentation using Deep Learning: A Hybrid Model.

Accurate brain tumor segmentation from MRI scans is crucial for diagnosis, treatment planning, and prognosis. However, manual segmentation is labor-intensive, prone to variability, and susceptible to errors, emphasizing the need for automated solutions. This study proposes the development and evaluation of deep learning architectures for brain tumor segmentation using the BraTS 2020 dataset. We propose and compare two approaches: (1) a standalone 3D U-Net, which processes 3D MRI volumes, and (2) a hybrid 2D model that combines a pre-trained VGG19 encoder with a U-Net decoder for 2D slice-based segmentation. The hybrid model leverages VGG19’s powerful feature extraction capabilities while maintaining the structural advantages of U-Net. To optimize the hybrid model, we applied a data transformation strategy that transforms volumetric MRI data into 2D slices, aligning with the inherent architecture of VGG19. The segmentation models were trained and evaluated using three out of four MRI modalities from the BraTS 2020 dataset, T1Gd, T2, and FLAIR, which provide complementary information for accurate tumor structure identification. Performance evaluation showed that the 3D U-Net exhibits limitations in segmenting tumor subregions, achieving Dice Similarity Coefficients (DSC) of 0.7870 (Necrotic and Non-Enhancing Tumor Core, NCR/NET), 0.8609 (Edema, ED), and 0.8058 (Enhancing Tumor, ET), with corresponding Intersection over Union (IoU) scores of 0.6489, 0.7558, and 0.6747, respectively. While the model demonstrates high specificity across all tumor classes, its lower sensitivity suggests the need for improvements to detect all instances of pathological tissue more effectively. On the other hand, the hybrid U-Net + VGG19 model demonstrates clearly superior segmentation results, outperforming many existing methods. It achieved DSC scores of 0.9078 (NCR/NET), 0.9245 (ED), and 0.9175 (ET), consistently exceeding 0.91 across key tumor subregions. Additionally, the hybrid model achieves superior segmentation performance across the three tumor structures, whole tumor (WT), tumor core (TC), and enhancing tumor (ET), achieving DSC scores of 0.9620, 0.9550, and 0.9175, respectively. As observed from the performance evaluation results of the models, the hybrid U-Net with VGG19 provides a computationally efficient and highly accurate approach for brain tumor segmentation.