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Biomed Opt Express. 2019 Jan 24;10(2):879-891. doi: 10.1364/BOE.10.000879. eCollection 2019 Feb 1.

Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp.

Author information

1
Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science & Technology, Daegu, 42988, South Korea.
2
Department of Dermatology, Seoul National University College of Medicine, Institute of Human-Environment Interface Biology, Seoul National University, Seoul, 03080, South Korea.
3
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.
4
ivymed27@snu.ac.kr.
5
jyhwang@dgist.ac.kr.

Abstract

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.

Conflict of interest statement

The authors declare that there are no conflicts of interest related to this article.

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