Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis
Sojeong Park, Shier Nee Saw, Xiuting Li, M Paknezhad, Davide Coppola, U. S. Dinish, Amalina Binte Ebrahim Attia, Yik Weng Yew, Steven Tien Guan Thng, Hwee Kuan Lee, Malini Olivo
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.