Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
Issued Date
2026-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105026713927
Pubmed ID
41345213
Journal Title
Scientific Reports
Volume
16
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.16 No.1 (2026)
Suggested Citation
Yodrabum N., Wongpraparut C., Titijaroonroj T., Chularojanamontri L., Bunyaratavej S., Silpa-archa N., Chaiyabutr C., Noraset T., Paringkarn T., Hutachoke T., Watchirakaeyoon P., Kobkurkul P., Apichonbancha S., Chiowchanwisawakit P. Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema. Scientific Reports Vol.16 No.1 (2026). doi:10.1038/s41598-025-29562-6 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114481
Title
Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Accurately differentiating scaly erythematous rashes among psoriasis, eczema, and dermatophytosis remains a clinical challenge, particularly for non-dermatologists. This study aimed to develop and evaluate deep learning models using macroscopic clinical images to classify these conditions and compare their performance with that of non-specialists. A total of 2940 images were sourced from public datasets, the Siriraj Dermatology databank, and newly collected images from Thai participants. Among sixteen evaluated models, the Swin demonstrated the best performance and interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model focused on clinically relevant lesion features. Most importantly, in a pilot comparison, the Swin outperformed non-specialists in diagnostic accuracy. However, given the limited sample size of 30 images and 30 evaluators, these results should be interpreted as exploratory. Future studies with larger datasets and diverse clinician cohorts are warranted to confirm these findings and to support clinical integration.