Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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,&nbsp;Nur Mohammad Fahad,&nbsp;Mohaimenul Azam Khan Raiaan,&nbsp;Mirjam Jonkman,&nbsp;Friso De Boer,&nbsp;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.

Abstract Image

结合一种新的无监督分类和增强的成像技术推进皮肤癌检测
皮肤癌是一种严重的健康威胁,如果不被发现,可能会迅速扩散。因此,早期发现可导致先进和有效的诊断,从而降低死亡率。无监督分类技术分析广泛的皮肤图像数据集,在没有事先标记的情况下识别模式和异常,促进早期发现和有效诊断,并可能挽救生命。在这项研究中,作者旨在探索无监督学习方法在皮肤镜图像中对不同类型皮肤病变进行分类的潜力。作者旨在通过引入提高图像质量和改进特征提取的创新技术来弥合皮肤病学研究的差距。为了实现这一目标,增强的超分辨率生成对抗网络(ESRGAN)被微调以增强皮肤病变图像的分辨率,使关键特征更明显。作者提取了直方图特征来捕捉基本的颜色特征,并使用Davies-Bouldin指数和剪影评分来确定最佳的聚类。直方图特征空间中具有欧氏距离的微调k-means聚类在ISIC2019和HAM10000数据集上的测试准确率分别达到87.77%和90.5%。无监督方法有效地对皮肤病变进行了分类,这表明无监督学习可以通过在没有大量人工注释的情况下进行早期检测和分类来显著推进皮肤病学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信