用深度学习精准识别肠胃病图像，还能解释判断依据。
DeepGI: Explainable Deep Learning for Gastrointestinal Image Classification
- 基于4000张内镜图训练VGG16等模型，解决光照角度等实际问题。
- 最优模型准确率达96.5%，在复杂条件下仍保持高精度。
- 通过Grad-CAM可视化关键区域，提升医生对AI决策的信任度。
本文针对一个包含4,000张内镜图像的新胃肠道医学影像数据集，开展全面的深度学习模型对比分析，涵盖憩室病、肿瘤、腹膜炎和输尿管四种关键疾病类别。利用先进深度学习技术，应对光照不均、相机角度变化及成像伪影等常见内镜挑战。表现最佳的VGG16与MobileNetV2模型测试准确率均达96.5%，Xception模型达到94.24%，为自动化疾病分类建立了稳健基准。研究还引入可解释AI方法（Grad-CAM），可视化模型预测所依赖的关键图像区域，显著提升临床可解释性。实验表明，该方法在复杂真实场景下仍具备鲁棒、准确且可解释的医学图像分析能力。本工作贡献了原创基准、对比洞见与可视化解释，推动胃肠道辅助诊断发展，凸显多样化临床数据集与模型可解释性在医疗AI中的重要性。
原文摘要 · Abstract (English)
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.
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