An Explainable AI Based Deep Ensemble Transformer Framework for Gastrointestinal Disease Prediction from Endoscopic Images
Bibliographic record
Abstract
Gastrointestinal diseases such as gastroesophageal reflux disease (GERD) and polyps remain prevalent and challenging to diagnose accurately due to overlapping visual features and inconsistent endoscopic image quality. In this study, we investigate the application of transformer-based deep learning models—Vision Transformer (ViT), Swin Transformer, and a novel Ensemble Transformer model—for classifying four categories: GERD, GERD Normal, Polyp, and Polyp Normal from endoscopic images. The dataset was curated and collected in collaboration with Zainul Haque Sikder Women's Medical College & Hospital, ensuring high-quality clinical annotations. All models were evaluated using precision, recall, F1 score, and overall classification accuracy. Our proposed Ensemble Transformer model, which fuses the outputs of ViT and Swin Transformer, achieved superior performance by delivering well-balanced F1 scores across all classes, reducing misclassification, and improving robustness with an overall accuracy of 87%. Furthermore, we incorporated explainable AI (XAI) techniques such as Grad-CAM and Grad-CAM++ to generate visual explanations of the model’s predictions, enhancing interpretability for clinical validation. This work demonstrates the potential of integrating global and local attention mechanisms along with XAI in building reliable, real-time, AI-assisted diagnostic support systems for gastrointestinal disorders, particularly in resource-limited healthcare settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".