A hybrid ViT-L/32-MaxViT-L architecture with adaptive gated fusion for multiclass gastrointestinal disease detection and multi-method post-hoc explainability.

INTRODUCTION

Accurate detection of gastrointestinal diseases from endoscopic images remains challenging due to substantial inter-class similarity, intra-class variability, and heterogeneous lesion presentation. While convolutional neural networks (CNNs) effectively capture fine mucosal textures, their limited receptive field restricts global contextual reasoning. Conversely, transformer architectures model long-range dependencies but may underrepresent localized structural detail.

METHODS

This study proposes a dual-branch hybrid transformer framework that integrates global token-level reasoning from ViT-L/32 with hierarchical spatial modeling from MaxViT-L. An adaptive gated fusion mechanism is introduced to dynamically regulate the relative contribution of local and global representations on a per-sample basis, enabling content-aware cross-scale integration. The model was evaluated on a multi-class gastrointestinal endoscopic dataset using comprehensive quantitative metrics, class-wise analysis, ablation studies, confidence interval estimation, and statistical validation. Post-hoc explainability was further investigated using multiple complementary XAI techniques.

RESULTS

The proposed framework demonstrated consistently high accuracy, sensitivity, specificity, and area under the ROC curve across eight gastrointestinal disease categories, outperforming several state-of-the-art transformer and hybrid architectures. The XAI analyses further confirmed lesion-focused activation consistency and stable decision-making.

DISCUSSION: The findings indicate that adaptive local-global feature integration through gated fusion enhances both diagnostic performance and interpretability, supporting the potential of hybrid transformer architectures for reliable computer-aided gastrointestinal disease classification.