Explainable GI lesion classification via class prototype guided hybrid feature fusion on Kvasir datasets
Year: 2026
Type: article
Abstract: Accurate and automated classification of gastrointestinal (GI) diseases from endoscopic images is crucial for supporting clinical diagnosis and reducing inter-observer variability. This study presents ClinFusion-Net, a hybrid deep learning framework that integrates convolutional neural networks and vision transformers for multi-class GI disease classification. ConvNeXt-XL extracts fine-grained local features, whereas Swin-Large captures broader contextual information. Their representations are fused through a class-prototype-guided MedicalAttention mechanism that assigns greater weight to diagnostically relevant features. Training employs a composite MedicalLoss objective combining weighted cross-entropy, focal loss, and attention-entropy regularization to improve class sensitivity and predictive calibration. ClinFusion-Net was trained and evaluated separately on Kvasir v1 and its expanded release, Kvasir v2, using release-specific data partitions, achieving an accuracy of 95.1% and a macro-AUC of 0.998 on Kvasir v2, and an accuracy of 96.3% and macro-AUC of 0.998 on Kvasir v1. On Kvasir v2, its accuracy was comparable to that of the strongest single-backbone baseline (95.1% versus 95.0%), while maintaining strong class-balanced discrimination through the integrated local–global representation. Because Kvasir v1 and Kvasir v2 are related releases from the same dataset family, these results are presented as separate within-release evaluations rather than as evidence of cross-dataset generalization. Controlled ablations identify feature fusion as the principal contributor to performance, with class-prototype attention and the composite objective providing complementary gains in calibration and difficult-class sensitivity. Furthermore, model interpretability is enhanced through Grad-CAM, LIME, and t-SNE embedding visualizations, which collectively provide spatial, perturbation-based, and feature-level evidence that predictions are associated with diagnostically relevant mucosal regions. Overall, the proposed framework demonstrates a high-performing, and interpretable solution for automated GI disease classification, with strong potential to support clinical decision-making and facilitate earlier diagnosis in real-world settings.
Source: Discover Applied Sciences
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7225626984
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