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

Authors

• Sojeong Park
• Shier Nee SawFollow
• Xiuting Li
• Mahsa Paknezhad
• Davide Coppola
• U. S. Dinish
• Amalina Binite Ebrahim Attia
• Yik Weng Yew
• Steven Tien Guan Thng
• Hwee Kuan Lee
• Malini Olivo

Document Type

Article

Publication Date

6-1-2021

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.

Keywords

Atopic dermatitis (AD), Skin inflammatory disease, Raster-scanning optoacoustic mesoscopy (RSOM), Dermatological imaging

Publication Title

Biomedical Optics Express

Recommended 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; and Olivo, Malini, "Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis" (2021). Research Publications (2021 to 2025). 12400.
https://knova.um.edu.my/research_publications_2021_2025/12400

Divisions

fsktm

Volume

12

Issue

6