Interpretable Gallbladder Ultrasound Diagnosis: A Lightweight Web-Mobile Software Platform with Real-Time XAI

TL;DR

This paper presents a lightweight, real-time AI diagnostic platform for gallbladder ultrasound images that combines high accuracy with interpretability, accessible via web and mobile devices to aid clinical decisions.

Contribution

It introduces a hybrid deep learning model with XAI visualizations for accurate, interpretable gallbladder disease classification in a portable software platform.

Findings

Achieves 99.85% accuracy on classification task

Operates with only 2.24 million parameters

Available as web and mobile applications for point-of-care use

Abstract

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