基于智能手机的多光谱成像及基于机器学习的分析用于鉴别头皮脂溢性皮炎和银屑病
Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp.
作者信息
Kim Sewoong, Kim Jihun, Hwang Minjoo, Kim Manjae, Jin Jo Seong, Je Minkyu, Jang Jae Eun, Lee Dong Hun, Hwang Jae Youn
机构信息
Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science & Technology, Daegu, 42988, South Korea.
Department of Dermatology, Seoul National University College of Medicine, Institute of Human-Environment Interface Biology, Seoul National University, Seoul, 03080, South Korea.
出版信息
Biomed Opt Express. 2019 Jan 24;10(2):879-891. doi: 10.1364/BOE.10.000879. eCollection 2019 Feb 1.
For appropriate treatment, accurate discrimination between seborrheic dermatitis and psoriasis in a timely manner is crucial to avoid complications. However, when they occur on the scalp, differential diagnosis can be challenging using conventional dermascopes. Thus, we employed smartphone-based multispectral imaging and analysis to discriminate between them with high accuracy. A smartphone-based multispectral imaging system, suited for scalp disease diagnosis, was redesigned. We compared the outcomes obtained using machine learning-based and conventional spectral classification methods to achieve better discrimination. The results demonstrated that smartphone-based multispectral imaging and analysis has great potential for discriminating between these diseases.
为了进行恰当治疗，及时准确地区分脂溢性皮炎和银屑病对于避免并发症至关重要。然而，当它们出现在头皮上时，使用传统皮肤镜进行鉴别诊断可能具有挑战性。因此，我们采用基于智能手机的多光谱成像和分析技术来高精度地区分它们。重新设计了一种适用于头皮疾病诊断的基于智能手机的多光谱成像系统。我们比较了使用基于机器学习的光谱分类方法和传统光谱分类方法所获得的结果，以实现更好的鉴别。结果表明，基于智能手机的多光谱成像和分析在区分这些疾病方面具有巨大潜力。
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