Mamba-based state-space modeling with window attention for explainable multi-class gastrointestinal disease diagnosis in endoscopic images.

INTRODUCTION

This paper introduces a new hybrid deep learning architecture, which is named GINet, to accurately and efficiently classify gastrointestinal (GI) diseases using endoscopic images. Automated diagnosis is not an easy task due to the visual similarity among GI conditions and the class imbalance.

METHOD: To overcome these challenges, the proposed model is based on EfficientNetV2 for powerful feature extraction, supported by a window-based attention mechanism to capture fine-grained local spatial dependencies. Moreover, a state-space module based on Mamba is added to model long-range global contextual relationships with linear computational complexity. It presents a complete data preprocessing pipeline, including class filtering, imbalance management via augmentation, and a leakage-free train-test split. The dataset consists of nine clinically relevant GI classes, with equal distribution for training. The extracted features are further refined by a channel projection layer, and the combined Window Attention and Mamba modules enable the model to learn local and global representations.

RESULTS

Extensive experiments show that the proposed GINet achieves 99.41% validation accuracy after classifier fine-tuning and 93% accuracy on the held-out test set, and high precision, recall, and F1 scores across all classes. Statistical analysis shows that the gains in performance are significant compared to the baseline models. Also, explainable artificial intelligence, as demonstrated by Grad-CAM, shows that the model attends to clinically meaningful regions, promoting transparency and trust.

DISCUSSION: The suggested solution offers a computationally efficient, accurate and interpretable solution to computer-aided diagnosis of gastrointestinal diseases, and represents a step toward computer-aided diagnosis of gastrointestinal diseases, subject to further validation on external, multi-center data.