Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis
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...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/26249/ https://doi.org/10.1364/BOE.415105 |
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| Summary: | 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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