Park, Sojeong and Saw, Shier Nee and Li, Xiuting and Paknezhad, Mahsa and Coppola, Davide and Dinish, U. S. and Ebrahim Attia, Amalina Binite and Yew, Yik Weng and Guan Thng, Steven Tien and Lee, Hwee Kuan and Olivo, Malini (2021) Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis. Biomedical Optics Express, 12 (6). p. 3671. ISSN 2156-7085, DOI https://doi.org/10.1364/BOE.415105.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Atopic dermatitis (AD); Skin inflammatory disease; Raster-scanning optoacoustic mesoscopy (RSOM); Dermatological imaging |
| Subjects: | R Medicine > RL Dermatology T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Feb 2022 07:56 |
| Last Modified: | 21 Feb 2022 07:56 |
| URI: | http://eprints.um.edu.my/id/eprint/26249 |
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