OVT-Net: Semantic segmentation of gastrointestinal cancer using an optimized vision transformer model with explainable AI

Gastric and intestinal cancers are among the deadliest gastrointestinal diseases, necessitating precise organ segmentation for effective detection and treatment planning. Conventional deep-learning models, such as CNN-based U-Net architectures, struggle with long-range dependencies and complex anatomical variations. This study introduces OVT-Net (Optimized Vision Transformer Network), an innovative deep-learning framework integrating Swin Transformer blocks, EfficientNetB7, Adaptive Contextual Attention (ACA) module, Atrous Spatial Pyramid Fusion (ASPF), and Squeeze-and-Excite (SE) Networks. Unlike traditional architectures, OVT-Net employs a hybrid dual-encoder structure, combining EfficientNetB7 for low-level feature extraction and Swin Transformers for global context modeling, addressing intricate anatomical complexities and imaging variabilities of the gastrointestinal tract. The model is trained on 38,496 MRI/CT scans paired with RLE-encoded masks that contain structural and labeling inconsistencies. These inconsistencies are resolved through a comprehensive preprocessing pipeline incorporating path generation, label restructuring, and augmentation to improve generalizability. Experimental results demonstrate superior performance, with a Dice score of 0.9350, an IoU score of 0.9218, a BCE loss of 0.0716, and robust surface distance metrics (HD95 and ASSD), outperforming conventional segmentation methods. To enhance clinical applicability, Explainable AI (XAI) techniques, including Grad-CAM and Grad-CAM++, provide interpretability by highlighting critical regions, improving model transparency in decision-making. Furthermore, OVT-Net is deployed in a Django-based web application, facilitating real-time segmentation and classification with an average accuracy of 97.5 %. This research presents OVT-Net as a transformative AI-driven segmentation model, bridging advanced vision transformers with XAI for enhanced medical diagnostics. Its integration into real-world clinical settings paves the way for improved cancer detection and early intervention.