Early detection support for hand-foot syndrome by fingertip skin feature analysis

DOI
  • HONDA Yuya
    Graduate School of Informatics and Engineering, The University of Electro-Communications
  • HATAKASHI Hiroki
    Artificial Intelligence eXploration Reserch Center, The University of Electro-Communications

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Other Title
  • 指先皮膚特徴解析による手足症候群の早期発見支援

Abstract

In this paper, we propose a method to support early detection of hand-foot syndrome by identifying the manifestation of the syndrome using the fingertip region where symptoms appear in images of the affected area taken at home. First, we detect the color chart in the image and correct the color difference between each image by color correction. Next, we estimate the joint points of the hand and extract the fingertip region of the target based on the joint points. Then, skin features that are indicators of symptom expression are extracted from the fingertip region. Finally, the extracted features are used to determine the onset of symptoms. As a result, the best performance of 0.82 for Recall and 0.66 for F-measure was obtained when SVM was used to classify the R channel using the feature size of the R channel relative to the B channel after color correction.

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