TAU-EffNetB7: A Novel Triple Attention U-Net Approach Using EfficientNetB7 for Enhanced Polyp Segmentation

Polyp segmentation is a critical but challenging process in clinical imaging since colonoscopic images are inherently complex and heterogeneous. Conventional single-stage segmentation networks lack good generalization and achieve only acceptable accuracy, particularly for small or uncertain polyps. To address these constraints, we propose two new models: TAU-EffNetB7 and TAU-EffNetB7 + Residual. These models apply triple-attention U-Net and triple-attention residual architectures, respectively, and incorporate cascaded stages, attention and residual operations, Atrous Spatial Pyramid Pooling, and transfer learning from EfficientNetB7. The multi-stage architecture enables progressive refinement of segmentations, better capture of multi-scale features, and accurate depiction of intricate boundaries. We evaluate our models on three publicly available colonoscopic datasets: Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB. The TAU-EffNetB7 attains Dice Similarity Coefficients (DSC) of 89.54%, 94.62%, and 94.68% on each dataset, respectively. The TAU-EffNetB7 + Residual model performs even better, achieving DSCs of 91.11%, 93.74%, and 94.72%, significantly outperforming baseline models such as U-Net and Attention U-Net. To assess generalization, we carry out experiments where models are trained with small subsets of data (Kvasir-SEG1, CVC-ClinicDB1, and CVC-ColonDB1) and tested on the full datasets. Both models demonstrate strong performance even with limited training data. TAU-EffNetB7 achieves 90.18% DSC when trained on Kvasir-SEG1, whereas TAU-EffNetB7 + Residual achieves 94.17% on CVC-ClinicDB and 94.68% on CVC-ColonDB when trained on their respective subsets. Notably, the residual-augmented model outperforms its counterpart in all but a few low-data scenarios.