A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.
Skin diseases are a significant global public health concern, affecting 21–85% of the world's population, particularly those in low- and middle-income countries. Accurate and timely diagnosis is crucial for effective treatment and improved patient outcomes. This study introduces a novel deep-learnin...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1776 - 1796 |
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| Autores principales: | , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280536&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280536 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280536 185280536 185280536 10.1007/s10278-024-01300-w 185280536 ppf: 1776 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases. aug: au: Badr, Mohamed Elkasaby, Abdullah Alrahmawy, Mohammed El-Metwally, Sara affil: https://ror.org/01k8vtd75 Computer Science Department, Faculty of Computers and Information, Mansoura University, 35516, Mansoura, Egypt sug: subj: Skin Diseases Diagnosis Skin Diseases Classification Deep Learning Methods Image Interpretation, Computer Assisted Methods Diagnosis, Computer Assisted Machine Learning Human Neural Networks (Computer) Machine Learning Algorithms Information Science Automation Skin Neoplasms Classification Skin Neoplasms Diagnosis Descriptive Statistics Prediction Models Information Resources Image Enhancement Dermatology Diagnostic Imaging Methods Hidradenitis Suppurativa Facial Dermatoses Acne Vulgaris Seborrhea Acne Rosacea Skin Diseases, Vesiculobullous ab: Skin diseases are a significant global public health concern, affecting 21–85% of the world's population, particularly those in low- and middle-income countries. Accurate and timely diagnosis is crucial for effective treatment and improved patient outcomes. This study introduces a novel deep-learning multi-model architecture designed for high-precision skin disease diagnosis. The system employs a five-category Xception model to classify skin lesions into five classes: Atopic Dermatitis, Acne and Rosacea, Skin Cancer, Bullous, and Others. Trained on 25,010 images, the model achieved 95% accuracy and an AUROC of 99.4%. To further enhance accuracy, transfer learning was applied, resulting in specialized models for each class, with strong performance across 40 skin conditions. Specifically, the Acne and Rosacea model achieved an accuracy of 90.0%, with a precision of 90.7%, recall of 90.1%, f1-score of 90.2%, and an AUROC of 99.0%. The Skin Cancer model demonstrated 94.0% accuracy, 94.8% precision, 94.2% recall, 94.1% f1-score, and a 99.5% AUROC. The Atopic Dermatitis model reported 91.8% accuracy, 92.2% precision, 91.8% recall, 91.9% f1-score, and a 98.8% AUROC. Finally, the Bullous model showed 90.0% accuracy, 90.6% precision, 90.0% recall, 90.0% f1-score, and a 98.9% AUROC. This approach surpasses previous studies, offering a more comprehensive diagnostic tool for skin diseases. To facilitate result reproducibility, the training and testing codes for the models utilized in this study are accessible via the GitHub repository (https://github.com/SaraEl-Metwally/A-Multi-Model-Deep-Learning-for-Diagnosing-Skin-Diseases). pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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