Mobile Image Analysis Application for Mantoux Skin Test
Liong Gele, Tan Chye Cheah

TL;DR
This paper introduces a mobile app that automates and improves the accuracy of Mantoux Skin Test interpretation for tuberculosis diagnosis using advanced image processing and machine learning, especially beneficial in resource-limited settings.
Contribution
The app uniquely combines ARCore, DeepLabv3, and edge detection to standardize and automate TST measurement, addressing limitations of manual and previous digital methods.
Findings
Significantly improved measurement accuracy over traditional methods
Enhanced reliability in induration detection using machine learning
Potential to increase TB diagnosis accessibility in resource-limited regions
Abstract
This paper presents a newly developed mobile application designed to diagnose Latent Tuberculosis Infection (LTBI) using the Mantoux Skin Test (TST). Traditional TST methods often suffer from low follow-up return rates, patient discomfort, and subjective manual interpretation, particularly with the ball-point pen method, leading to misdiagnosis and delayed treatment. Moreover, previous developed mobile applications that used 3D reconstruction, this app utilizes scaling stickers as reference objects for induration measurement. This mobile application integrates advanced image processing technologies, including ARCore, and machine learning algorithms such as DeepLabv3 for robust image segmentation and precise measurement of skin indurations indicative of LTBI. The system employs an edge detection algorithm to enhance accuracy. The application was evaluated against standard clinical…
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