Please use this identifier to cite or link to this item: https://doi.org/10.1364/boe.415105
Title: Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis
Authors: 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 
Olivo, Malini
Issue Date: 27-May-2021
Publisher: The Optical Society
Citation: 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, Olivo, Malini (2021-05-27). Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis. Biomedical Optics Express 12 (6) : 3671-3683. ScholarBank@NUS Repository. https://doi.org/10.1364/boe.415105
Rights: Attribution 4.0 International
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.
Source Title: Biomedical Optics Express
URI: https://scholarbank.nus.edu.sg/handle/10635/232457
ISSN: 2156-7085
DOI: 10.1364/boe.415105
Rights: Attribution 4.0 International
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