Explainable Framework for Brain Tumour Detection and Segmentation from Multimodal MRI
Keywords:
Brain Tumour Detection, Explainable AI, Medical Image Segmentation, MiniUNet, Multimodal MRI, Deep Learning, Grad-CAM, Computer-Aided DiagnosisAbstract
Early detection of brain tumours plays a vital role in improving treatment planning and patient survival. Magnetic Resonance Imaging (MRI) provides detailed structural information about brain tissues and is widely used for tumour diagnosis. However, manual analysis of MRI scans is time-consuming and may lead to inconsistencies due to observer variability. Deep learning approaches have recently demonstrated strong performance in medical image analysis tasks such as tumour detection, segmentation, and classification. Despite their high predictive accuracy, most deep neural networks behave as black-box systems and provide limited interpretability for clinical decision making. This research proposes an explainable deep learning framework based on the MiniUNet architecture for automated brain tumour detection and segmentation using multimodal MRI data. The proposed framework integrates segmentation and classification within a unified architecture while incorporating explainability mechanisms to improve clinical transparency. The system processes four MRI modalities—T1, T1-contrast enhanced (T1c), T2, and FLAIR—to capture complementary anatomical information about tumour structures. A region-of-interest (ROI) guided classification module ensures that diagnostic predictions are derived from tumour-specific regions extracted by the segmentation network. Experiments are conducted using the BraTS 2021 dataset, which contains expert-annotated MRI volumes of glioma patients. The dataset includes voxel-level segmentation masks representing whole tumour, tumour core, and enhancing tumour regions. Performance evaluation demonstrates that the proposed MiniUNet model achieves superior segmentation performance compared with baseline architectures such as U-Net and Attention U-Net. The model obtains a Dice score of 91.0% and improves localization accuracy through attention-guided feature learning.To enhance interpretability, Grad-CAM based visual explanations are incorporated, enabling clinicians to visualize the image regions responsible for classification decisions. The experimental results demonstrate that the proposed framework provides a balanced combination of accuracy, interpretability, and computational efficiency, making it suitable for deployment in computer-aided diagnostic systems.
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