Interpretable Gallbladder Ultrasound Diagnosis: A Lightweight Web-Mobile Software Platform with Real-Time XAI
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912793221922816 |
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| author | Bhoyan, Fuyad Hasan Sarker, Prashanta Ethila, Parsia Noor Hossain, Md. Emon Hossain, Md Kaviul Mehedi, Md Humaion Kabir |
| author_facet | Bhoyan, Fuyad Hasan Sarker, Prashanta Ethila, Parsia Noor Hossain, Md. Emon Hossain, Md Kaviul Mehedi, Md Humaion Kabir |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Gallbladder Ultrasound Diagnosis: A Lightweight Web-Mobile Software Platform with Real-Time XAI Bhoyan, Fuyad Hasan Sarker, Prashanta Ethila, Parsia Noor Hossain, Md. Emon Hossain, Md Kaviul Mehedi, Md Humaion Kabir Software Engineering Computer Vision and Pattern Recognition 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 |
| title | Interpretable Gallbladder Ultrasound Diagnosis: A Lightweight Web-Mobile Software Platform with Real-Time XAI |
| topic | Software Engineering Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.23033 |