An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input

Introduction The diagnosis of melasma is often based on the naked-eye judgment of physicians. However, this is a challenge for inexperienced physicians and non-professionals, and incorrect treatment might have serious consequences. Therefore, it is important to develop an accurate method for melasma...

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Published in:Dermatology and therapy Vol. 13; no. 2; pp. 569 - 579
Main Authors: Liu, Lin, Liang, Chen, Xue, Yuzhou, Chen, Tingqiao, Chen, Yangmei, Lan, Yufan, Wen, Jiamei, Shao, Xinyi, Chen, Jin
Format: Journal Article
Language:English
Published: Cheshire Springer Healthcare 01.02.2023
Springer
Springer Nature B.V
Subjects:
Accuracy
Computer-aided medical diagnosis
Deep learning
Dermatology
Diagnosis
Image processing
Internal Medicine
Light
Machine learning
Medicine
Medicine & Public Health
Melanosis
Methods
Oral and Maxillofacial Surgery
Original Research
Patients
Physicians
Plastic Surgery
Quality of Life Research
Skin
China
Deep learning
Melasma
Diagnosis
Convolutional neural network
Network performance
ISSN:2193-8210, 2190-9172
Online Access:Get full text
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Summary:Introduction The diagnosis of melasma is often based on the naked-eye judgment of physicians. However, this is a challenge for inexperienced physicians and non-professionals, and incorrect treatment might have serious consequences. Therefore, it is important to develop an accurate method for melasma diagnosis. The objective of this study is to develop and validate an intelligent diagnostic system based on deep learning for melasma images. Methods A total of 8010 images in the VISIA system, comprising 4005 images of patients with melasma and 4005 images of patients without melasma, were collected for training and testing. Inspired by four high-performance structures (i.e., DenseNet, ResNet, Swin Transformer, and MobileNet), the performances of deep learning models in melasma and non-melasma binary classifiers were evaluated. Furthermore, considering that there were five modes of images for each shot in VISIA, we fused these modes via multichannel image input in different combinations to explore whether multimode images could improve network performance. Results The proposed network based on DenseNet121 achieved the best performance with an accuracy of 93.68% and an area under the curve (AUC) of 97.86% on the test set for the melasma classifier. The results of the Gradient-weighted Class Activation Mapping showed that it was interpretable. In further experiments, for the five modes of the VISIA system, we found the best performing mode to be “BROWN SPOTS.” Additionally, the combination of “NORMAL,” “BROWN SPOTS,” and “UV SPOTS” modes significantly improved the network performance, achieving the highest accuracy of 97.4% and AUC of 99.28%. Conclusions In summary, deep learning is feasible for diagnosing melasma. The proposed network not only has excellent performance with clinical images of melasma, but can also acquire high accuracy by using multiple modes of images in VISIA.
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ISSN:2193-8210
2190-9172
DOI:10.1007/s13555-022-00874-z