ResNet50-U-Net integrated deep residual framework for brain tumor subregion segmentation using multimodal MRI

Brain tumor subregion segmentation from multimodal magnetic resonance imaging (MRI) is important for glioma diagnosis, treatment planning, and disease monitoring. This study presents a ResNet50-U-Net integrated deep residual framework for segmenting Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions from multimodal MRI. Four MRI modalities, namely FLAIR, T1, T1ce, and T2, are combined as a four-channel input to transform the multimodal input compatible with the ResNet50 encoder. The proposed ResNet50-U-Net was compared with CNN and U-Net baselines using Dice Similarity Coefficient, Intersection over Union, HD95, precision, sensitivity, specificity, accuracy, parameter count, model size, and training time. The primary experiments were conducted on 368 labelled BraTS2020 cases using a patient-wise split of 257 training, 55 validation, and 56 independent test cases. Over tumor slice selection, 17,053 for training, 3,573 for validation, and 3,706 for testing were obtained. The proposed ResNet50-U-Net achieved the highest Dice scores for WT and ET segmentation, with values of 0.9100 and 0.8151, respectively, while the CNN achieved the highest TC Dice score of 0.7915. Cross-dataset generalization was evaluated on 95 independent BraTS-Africa glioma cases using an independently retrained instance of the same ResNet50-U-Net architecture, without fine-tuning or external threshold optimization. It is observed that patient-level Dice scores of 0.8592 ± 0.1674, 0.7387 ± 0.2500, and 0.6990 ± 0.2310 were obtained for WT, TC, and ET, respectively. These findings indicate that the proposed framework provides strong WT and ET segmentation on BraTS2020 and retains meaningful segmentation capability on an unseen external cohort, although cross-dataset robustness varies across tumor subregions.