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http://dx.doi.org/10.9717/kmms.2021.24.11.1492

Detecting Boundary of Erythema Using Deep Learning  

Kwon, Gwanyoung (Dept. of Medicine, Gachon University College of Medicine)
Kim, Jong Hoon (Dept. of Biomedical Eng., Gachon University College of Medicine)
Kim, Young Jae (Dept. of Biomedical Eng., Gachon University College of Medicine)
Lee, Sang Min (Dept. of Internal Medicine, Gachon University College of Medicine)
Kim, Kwang Gi (Dept. of Biomedical Eng., Gachon University College of Medicine)
Publication Information
Journal of Korea Multimedia Society / v.24, no.11, 2021 , pp. 1492-1499 More about this Journal
Abstract
Skin prick test is widely used in diagnosing allergic sensitization to common inhalant or food allergens, in which positivities are manually determined by calculating the areas or mean diameters of wheals and erythemas provoked by allergens pricked into patients' skin. In this work, we propose a segmentation algorithm over U-Net, one of the FCN models of deep learning, to help us more objectively grasp the erythema boundaries. The performance of the model is analyzed by comparing the results of automatic segmentation of the test data to U-Net with the results of manual segmentation. As a result, the average Dice coefficient value was 94.93%, the average precision and sensitivity value was 95.19% and 95.24% respectively. We find that the proposed algorithm effectively discriminates the skin's erythema boundaries. We expect this algorithm to play an auxiliary role in skin prick test in real clinical trials in the future.
Keywords
Skin prick test; Erythema; U-Net; Small computer;
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