Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation
Jun Zhang, Qian Jiang, Qiang Chen, Bin Hu, Liuqing Chen Department of Dermatology, Wuhan No. 1 hospital, Wuhan, Hubei, People’s Republic of ChinaCorrespondence: Bin Hu, Email binhu88@126.com Liuqing Chen, Email chlq35@126.comBackground: Melasma is a prevalent pigmentary disorder characterized by tre...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Dove Medical Press
2025-04-01
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| Series: | Clinical, Cosmetic and Investigational Dermatology |
| Subjects: | |
| Online Access: | https://www.dovepress.com/deep-learning-based-multiclass-framework-for-real-time-melasma-severit-peer-reviewed-fulltext-article-CCID |
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| Summary: | Jun Zhang, Qian Jiang, Qiang Chen, Bin Hu, Liuqing Chen Department of Dermatology, Wuhan No. 1 hospital, Wuhan, Hubei, People’s Republic of ChinaCorrespondence: Bin Hu, Email binhu88@126.com Liuqing Chen, Email chlq35@126.comBackground: Melasma is a prevalent pigmentary disorder characterized by treatment resistance and high recurrence. Existing assessment methods like the Melasma Area and Severity Index (MASI) are subjective and prone to inter-observer variability.Objective: This study aimed to develop an AI-assisted, real-time melasma severity classification framework based on deep learning and clinical facial images.Methods: A total of 1368 anonymized facial images were collected from clinically diagnosed melasma patients. After image preprocessing and MASI-based labeling, six CNN architectures were trained and evaluated using PyTorch. Model performance was assessed through accuracy, precision, recall, F1-score, AUC, and interpretability via Layer-wise Relevance Propagation (LRP).Results: GoogLeNet achieved the best performance, with an accuracy of 0.755 and an F1-score of 0.756. AUC values across severity levels reached 0.93 (mild), 0.86 (moderate), and 0.94 (severe). LRP analysis confirmed GoogLeNet’s superior feature attribution.Conclusion: This study presents a robust, interpretable deep learning model for melasma severity classification, offering enhanced diagnostic consistency. Future work will integrate multimodal data for more comprehensive assessment.Keywords: melasma, deep learning, convolutional neural networks, MASI, clinical decision support |
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| ISSN: | 1178-7015 |