Automatic Hand Landmark Detection for Leprosy Diagnosis
Comparison of Output Adaptation Techniques for Hand Keypoint Prediction
M. Tran (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T.C. Markhorst – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Jan C. Van Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Katai Liang – Graduation committee member (TU Delft - Cyber Security)
Z.Y. Lin – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Early detection of leprosy, a neglected tropical disease, is crucial to preventing irreversible nerve damage and disability. Analyzing temperature vari- ations in hands using infrared (IR) cameras offers a potential low-cost alternative to existing medical equipment for early detection of leprosy. This study explores the adaptation of hand landmark detec- tion models, commonly used for hand pose track- ing, to infer the hypothenar area, a critical region for leprosy diagnosis. The research addresses the challenge of limited ground-truth data for the hy- pothenar keypoint by developing annotated datasets and evaluating machine learning models like Lasso Regression and XGBoost. These models signif- icantly outperform the existing method of linear interpolation, demonstrating the feasibility of ac- curate hypothenar keypoint prediction even with limited training data. The findings contribute to the development of accessible, automated tools for early leprosy diagnosis, particularly in resource- constrained settings.