🤖 AI Summary
To address the low efficiency, poor inter-observer consistency, and insufficient standardization in manual interpretation of gastric histopathological images, this paper proposes an attention-enhanced hybrid ConvNeXtV2-Transformer model. Methodologically, we replace the MBConv blocks in CoAtNet with ConvNeXtV2 modules and integrate the CBAM channel-spatial joint attention mechanism, thereby preserving fine-grained local feature representation while enhancing global contextual modeling. The resulting end-to-end classification framework achieves 96.47% and 98.29% accuracy on the HMU-GC-HE-30K and GasHisSDB datasets, respectively, with a peak AUC of 99.90%. These results significantly surpass those of state-of-the-art CNNs and Vision Transformers. Our approach provides a highly accurate and robust solution for automated early diagnosis of gastric diseases.
📝 Abstract
Background and objective Early diagnosis of gastric diseases is crucial to prevent fatal outcomes. Although histopathologic examination remains the diagnostic gold standard, it is performed entirely manually, making evaluations labor-intensive and prone to variability among pathologists. Critical findings may be missed, and lack of standard procedures reduces consistency. These limitations highlight the need for automated, reliable, and efficient methods for gastric tissue analysis. Methods In this study, a novel hybrid model named CoAtNeXt was proposed for the classification of gastric tissue images. The model is built upon the CoAtNet architecture by replacing its MBConv layers with enhanced ConvNeXtV2 blocks. Additionally, the Convolutional Block Attention Module (CBAM) is integrated to improve local feature extraction through channel and spatial attention mechanisms. The architecture was scaled to achieve a balance between computational efficiency and classification performance. CoAtNeXt was evaluated on two publicly available datasets, HMU-GC-HE-30K for eight-class classification and GasHisSDB for binary classification, and was compared against 10 Convolutional Neural Networks (CNNs) and ten Vision Transformer (ViT) models. Results CoAtNeXt achieved 96.47% accuracy, 96.60% precision, 96.47% recall, 96.45% F1 score, and 99.89% AUC on HMU-GC-HE-30K. On GasHisSDB, it reached 98.29% accuracy, 98.07% precision, 98.41% recall, 98.23% F1 score, and 99.90% AUC. It outperformed all CNN and ViT models tested and surpassed previous studies in the literature. Conclusion Experimental results show that CoAtNeXt is a robust architecture for histopathological classification of gastric tissue images, providing performance on binary and multiclass. Its highlights its potential to assist pathologists by enhancing diagnostic accuracy and reducing workload.