Model Learning Analysis of 3D Optoacoustic Mesoscopy Images for the Classification of Atopic Dermatitis

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2021 Jul 5

PMID 34221687

Citations 3

Authors

Sojeong Park

Shier Nee Saw

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

Affiliations

Soon will be listed here.

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.

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