# DeepGI: Explainable Deep Learning for Gastrointestinal Image Classification

arXiv: https://arxiv.org/abs/2511.21959 · HTML: https://mlsanity.com/paper/2511.21959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [EndoDINO: A Foundation Model for GI Endoscopy](https://mlsanity.com/paper/2501.05488/) (2025)
- [EYE-DEX: Eye Disease Detection and EXplanation System](https://mlsanity.com/paper/2509.24136/) (2025)
- [Accelerating Cerebral Diagnostics with BrainFusion: A Comprehensive MRI Tumor Framework](https://mlsanity.com/paper/2509.24149/) (2025)
- [AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets](https://mlsanity.com/paper/2509.26185/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive comparative model analysis on a novel gastrointestinal medical imaging dataset, comprised of 4,000 endoscopic images spanning four critical disease classes: Diverticulosis, Neoplasm, Peritonitis, and Ureters. Leveraging state-of-the-art deep learning techniques, the study confronts common endoscopic challenges such as variable lighting, fluctuating camera angles, and frequent imaging artifacts. The best performing models, VGG16 and MobileNetV2, each achieved a test accuracy of 96.5%, while Xception reached 94.24%, establishing robust benchmarks and baselines for automated disease classification. In addition to strong classification performance, the approach includes explainable AI via Grad-CAM visualization, enabling identification of image regions most influential to model predictions and enhancing clinical interpretability. Experimental results demonstrate the potential for robust, accurate, and interpretable medical image analysis even in complex real-world conditions. This work contributes original benchmarks, comparative insights, and visual explanations, advancing the landscape of gastrointestinal computer-aided diagnosis and underscoring the importance of diverse, clinically relevant datasets and model explainability in medical AI research.