Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning

Autor: Hequn Huang, Changqing Wang, Geng Gao, Zhuangzhuang Fan, Lulu Ren, Rui Wang, Zhu Chen, Maoxin Huang, Mei Li, Fei Yang, Fengli Xiao
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Dermatology and Therapy, Vol 14, Iss 12, Pp 3307-3320 (2024)
Druh dokumentu: article
ISSN: 2193-8210
2190-9172
DOI: 10.1007/s13555-024-01296-9
Popis: Abstract Introduction There is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method. Methods A total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye. Results The improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P
Databáze: Directory of Open Access Journals
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