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

Divisions

fsktm

Publication Title

Biomedical Optics Express

Volume

12

Issue

6