Early and accurate detection of gallbladder diseases is crucial, yet ultrasound interpretation is challenging. To address this, an AI-driven diagnostic software integrates our hybrid deep learning model MobResTaNet to classify ten categories, nine gallbladder disease types and normal directly from ultrasound images. The system delivers interpretable, real-time predictions via Explainable AI (XAI) visualizations, supporting transparent clinical decision-making. It achieves up to 99.85% accuracy with only 2.24M parameters. Deployed as web and mobile applications using HTML, CSS, JavaScript, Bootstrap, and Flutter, the software provides efficient, accessible, and trustworthy diagnostic support at the point of care
翻译：胆囊疾病的早期精准检测至关重要，但超声影像判读具有挑战性。为此，本研究开发了一款AI驱动诊断软件，集成我们提出的混合深度学习模型MobResTaNet，可直接对超声图像进行十类别分类（九种胆囊疾病类型及正常状态）。该系统通过可解释人工智能（XAI）可视化技术提供可解释的实时预测，支持透明的临床决策。该模型仅需2.24M参数即达到最高99.85%的准确率。基于HTML、CSS、JavaScript、Bootstrap与Flutter框架部署为Web端与移动端应用，本软件能在诊疗现场提供高效、易用且可信赖的诊断支持。
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