Automated Classification of Skin Diseases Using Microscopic Images: A Machine Learning Approach

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dc.contributor.authorKarapinar Senturk, Zehra
dc.contributor.authorGuler, Recep
dc.contributor.authorOzcan, Yunus
dc.contributor.authorGamsizkan, Mehmet
dc.date.accessioned2025-10-11T20:48:50Z
dc.date.available2025-10-11T20:48:50Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study presents a machine learning-based approach for the automated classification of skin diseases, specifically targeting morphea and lichen sclerosus, using microscopic images. The proposed method involves a systematic workflow, including image preprocessing techniques such as resizing, Reinhard normalization, Gaussian filtering, and CLAHE histogram equalization to enhance image quality. Feature extraction was performed using Gray-Level Co-occurrence Matrix (GLCM) and histogram-based statistical methods, capturing texture and intensity characteristics of skin tissues. Several classification models, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Logistic Regression (LR), were evaluated using accuracy, precision, recall, and F1 score, with hyperparameter optimization via grid search. The experimental results revealed that the combined feature set (GLCM + Histogram) achieved the highest performance, with the RF and K-NN models yielding a 100% in all performance metrics, including accuracy, sensitivity, recall, and F1-score. The study introduces a novel approach by examining these two diseases simultaneously, offering a reliable tool to support dermatologists with accurate and quick diagnoses. Future work will focus on expanding the dataset, exploring advanced deep learning techniques, and integrating clinical metadata to enhance model generalizability.en_US
dc.description.sponsorshipDepartment of Dermatology of Duzce Universityen_US
dc.description.sponsorshipThe authors would like to express their sincere gratitude to the Department of Dermatology of Duzce University for their invaluable support and collaboration throughout this study. The provision of microscopic skin images and clinical insights greatly contributed to the success of this research. Special thanks are extended to Research Assistant Dr. Narges Rahnamaei Zonouz and medical school student Arda Yuksel for their meticulous efforts for data collection and dedication during the experimental phases. Their contributions were instrumental in ensuring the accuracy and relevance of the dataset, as well as providing clinical expertise that enriched our study's outcomes.en_US
dc.identifier.doi10.1002/cpe.70220
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue21-22en_US
dc.identifier.scopus2-s2.0-105012477232en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.70220
dc.identifier.urihttps://hdl.handle.net/20.500.12684/22131
dc.identifier.volume37en_US
dc.identifier.wosWOS:001561266200046en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrencyand Computation-Practice & Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectmachine learningen_US
dc.subjectskin diseaseen_US
dc.subjectwhole slide imageen_US
dc.titleAutomated Classification of Skin Diseases Using Microscopic Images: A Machine Learning Approachen_US
dc.typeArticleen_US

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