Md. Abdur Rahman, Nur Mohammad Fahad, Mohaimenul Azam Khan Raiaan, Mirjam Jonkman, Friso De Boer, Sami Azam
{"title":"Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques","authors":"Md. Abdur Rahman, Nur Mohammad Fahad, Mohaimenul Azam Khan Raiaan, Mirjam Jonkman, Friso De Boer, Sami Azam","doi":"10.1049/cit2.12410","DOIUrl":null,"url":null,"abstract":"<p>Skin cancer, a severe health threat, can spread rapidly if undetected. Therefore, early detection can lead to an advanced and efficient diagnosis, thus reducing mortality. Unsupervised classification techniques analyse extensive skin image datasets, identifying patterns and anomalies without prior labelling, facilitating early detection and effective diagnosis and potentially saving lives. In this study, the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic images. The authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature extraction. To achieve this, enhanced super-resolution generative adversarial networks (ESRGAN) was fine-tuned to strengthen the resolution of skin lesion images, making critical features more visible. The authors extracted histogram features to capture essential colour characteristics and used the Davies–Bouldin index and silhouette score to determine optimal clusters. Fine-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets, respectively. The unsupervised approach effectively categorises skin lesions, indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"474-493"},"PeriodicalIF":8.4000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12410","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cit2.12410","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Skin cancer, a severe health threat, can spread rapidly if undetected. Therefore, early detection can lead to an advanced and efficient diagnosis, thus reducing mortality. Unsupervised classification techniques analyse extensive skin image datasets, identifying patterns and anomalies without prior labelling, facilitating early detection and effective diagnosis and potentially saving lives. In this study, the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic images. The authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature extraction. To achieve this, enhanced super-resolution generative adversarial networks (ESRGAN) was fine-tuned to strengthen the resolution of skin lesion images, making critical features more visible. The authors extracted histogram features to capture essential colour characteristics and used the Davies–Bouldin index and silhouette score to determine optimal clusters. Fine-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets, respectively. The unsupervised approach effectively categorises skin lesions, indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.