Trans-Semantic residual deep structure for scalable brain cancer segmentation: BResU-Net

A significant rise in brain cancer cases over last few decades, insists early tumor detection, which is typically accomplished using vision-based computing and magnetic resonance imaging (MRI). Despite advances in deep learning for medical imaging, achieving accurate and timely tumor segmentation remains challenging due to issues like class imbalance, gradient vanishing, and premature convergence. Furthermore, limited works focus on simultaneous segmentation of the whole tumor and its substructures, which is essential for comprehensive diagnosis and treatment planning. To overcome these gaps, this paper proposes a Trans-Semantic Residual Deep Structure for Scalable Brain Tumor Segmentation namely BResUNet which is a combination of U-Net and ResUNet. This multi-stage deep network included sub-components like Pre-trained Activation Residual Layers (PARUL), attention gates, deep supervision, etc. The annotations followed by U-Net segmentation enable alleviating class imbalance, while Z-score normalization avoids the over-fitting problem. Architecturally, BResUNet is designed to perform segmentation of the whole tumor region as well as allied sub-structures as a multi-task problem. The ability to learn semantic contextual features as well as morphological features enable BResUNet to achieve optimal brain tumor segmentation results. To enable a scalable and time-efficient solution, BResUNet is executed over TensorFlow Distributed Computing Architecture with Multi-Task Mirroring (TD-CAM). Simulation results reveals the BResUNet’s higher performance with 95.37% accuracy, and 92.29% sensitivity, ensuring its applicability in real-time brain tumor diagnosis.