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
Park, Sojeong
Li, Xiuting
Paknezhad, Mahsa
Coppola, Davide
Dinish, U. S.
Ebrahim Attia, Amalina Binite
Yew, Yik Weng
Guan Thng, Steven Tien
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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
Series/Report No.
Collections
Rights
Attribution 4.0 International
Date
2021-05-27
DOI
10.1364/boe.415105
Type
Article