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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Dove Medical Press
2025-04-01
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| Series: | Clinical, Cosmetic and Investigational Dermatology |
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| 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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| author | Zhang J Jiang Q Chen Q Hu B Chen L |
| author_facet | Zhang J Jiang Q Chen Q Hu B Chen L |
| author_sort | Zhang J |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-247962ea667e480297691185bbebaef2 |
| institution | OA Journals |
| issn | 1178-7015 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Clinical, Cosmetic and Investigational Dermatology |
| spelling | doaj-art-247962ea667e480297691185bbebaef22025-08-20T02:20:13ZengDove Medical PressClinical, Cosmetic and Investigational Dermatology1178-70152025-04-01Volume 1810331044102522Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability EvaluationZhang JJiang QChen QHu BChen LJun 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 supporthttps://www.dovepress.com/deep-learning-based-multiclass-framework-for-real-time-melasma-severit-peer-reviewed-fulltext-article-CCIDmelasmadeep learningconvolutional neural networksmasiclinical decision support |
| spellingShingle | Zhang J Jiang Q Chen Q Hu B Chen L Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation Clinical, Cosmetic and Investigational Dermatology melasma deep learning convolutional neural networks masi clinical decision support |
| title | Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation |
| title_full | Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation |
| title_fullStr | Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation |
| title_full_unstemmed | Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation |
| title_short | Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation |
| title_sort | deep learning based multiclass framework for real time melasma severity classification clinical image analysis and model interpretability evaluation |
| topic | melasma deep learning convolutional neural networks masi clinical decision support |
| url | https://www.dovepress.com/deep-learning-based-multiclass-framework-for-real-time-melasma-severit-peer-reviewed-fulltext-article-CCID |
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