YongHo Kwon

10.5626/JOK.2023.50.1.72

vitiligo image segmentation Transfer Learning VASI score vitiligo

Vitiligo is a commonly acquired skin disorder that results from the loss of melanin pigment from the epidermis and is clinically indicated by pale or white patches on the body. Preliminary treatment is essential for vitiligo, but vitiligo does not cause pain or health problems. Therefore, vitiligo patents are treated when skin lesions are visible on the outside. The subjective judgment treats vitiligo of dermatologist’s, and there is no quantitative and objective analysis method through imaging, because it is difficult to obtain a medical image. Several diagnostic methods have been developed through a few medical studies. In this paper, we propose a method for area of vitiligo through image segmentation using metastasis learning to overcome the limitations of vitiligo medical data collection. The transfer learning model was selected by experimenting with the possibility of application to deep learning models such as U-net, FCN, and Deeplab. In addition, the severity of Vitiligo was measured using the VASI score used in the medical field, converting the skin image into an RGB skin image representing skin areas. In the experimental results, when trained with an imbalanced vitiligo image dataset, the performance of Deeplab, measured by F1-score and IoU, was superior to that of U-net and the image processing method. Additionally, the method for calculating the VASI score in vitiligo image proposed in this paper showed the possibility of being used for vitiligo diagnosis.