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Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis

Park, Sojeong
Saw, Shier Nee
Li, Xiuting
Paknezhad, Mahsa
Coppola, Davide
Dinish, U. S.
Ebrahim Attia, Amalina Binite
Yew, Yik Weng
Guan Thng, Steven Tien
Lee, Hwee Kuan... show 1 more
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Abstract
Atopic dermatitis (AD) is a skin inflammatory disease affecting 10% of the population worldwide. Raster-scanning optoacoustic mesoscopy (RSOM) has recently shown promise in dermatological imaging. We conducted a comprehensive analysis using three machine-learning models, random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) for classifying healthy versus AD conditions, and sub-classifying different AD severities using RSOM images and clinical information. CNN model successfully differentiates healthy from AD patients with 97% accuracy. With limited data, RF achieved 65% accuracy in sub-classifying AD patients into mild versus moderate-severe cases. Identification of disease severities is vital in managing AD treatment. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Keywords
Source Title
Biomedical Optics Express
Publisher
The Optical Society
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Rights
Attribution 4.0 International
Date
2021-05-27
DOI
10.1364/boe.415105
Type
Article
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