用深度学习和统计方法自动识别玫瑰痤疮，提升公众认知与早期干预
Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches
- 基于ResNet-18与主成分分析等方法，从面部图像中自动检测玫瑰痤疮
- 统计方法可解释特征差异，帮助医生和患者理解诊断结果
- 适合关注皮肤健康、早筛早治的普通人群及医疗应用开发者
据美国玫瑰痤疮协会统计，约1600万美国人患有玫瑰痤疮。为提升公众认知，本文提出一种结合深度学习与可解释统计方法的自动玫瑰痤疮检测技术。深度学习采用ResNet-18模型进行分类；统计方法则利用两类均值对比及主成分分析（PCA）提取面部图像特征。该方法具备三重贡献：一是能自动区分玫瑰痤疮患者与健康个体；二是统计方法提供可解释性，增强医患对结果的信任；三是有助于提升大众对玫瑰痤疮的认知，并提醒患者在疾病早期及时治疗——因早期干预效果更佳。代码与数据已公开于https://github.com/chengyuyang-njit/rosacea_detection.git。
原文摘要 · Abstract (English)
Approximately 16 million Americans suffer from rosacea according to the National Rosacea Society. To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper. The deep learning method applies the ResNet-18 for rosacea detection, and the statistical approaches utilize the means of the two classes, namely, the rosacea class vs. the normal class, and the principal component analysis to extract features from the facial images for automatic rosacea detection. The contributions of the proposed methods are three-fold. First, the proposed methods are able to automatically distinguish patients who are suffering from rosacea from people who are clean of this disease. Second, the statistical approaches address the explainability issue that allows doctors and patients to understand and trust the results. And finally, the proposed methods will not only help increase rosacea awareness in the general population but also help remind the patients who suffer from this disease of possible early treatment since rosacea is more treatable at its early stages. The code and data are available at https://github.com/chengyuyang-njit/rosacea_detection.git.
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