基于深度学习方法和二元哈里斯-霍克优化的黑色素瘤皮肤癌检测

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Noorah Jaber Faisal Jaber, Ayhan Akbas
{"title":"基于深度学习方法和二元哈里斯-霍克优化的黑色素瘤皮肤癌检测","authors":"Noorah Jaber Faisal Jaber, Ayhan Akbas","doi":"10.1007/s11042-024-19864-8","DOIUrl":null,"url":null,"abstract":"<p>The issue of skin cancer has garnered significant attention from the scientific community worldwide, with melanoma being the most lethal and uncommon form of the disease. Melanoma occurs due to the uncontrolled growth of melanocyte cells, which are responsible for imparting color to the skin. If left untreated, melanoma can spread throughout the body and cause death. Early detection of melanoma can lower its mortality rate. In this study, we propose a robust Convolutional Neural Network (CNN)-based method for classifying melanoma images as healthy or non-healthy. To train and test the model, we utilized public datasets from International Skin Imaging Collaboration (ISIC). Additionally, we compared our method with other classification techniques, including Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (K-NN), using the Harris Hawks Optimization algorithm. The results of our method showed superior performance compared to the other approaches.</p>","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Melanoma skin cancer detection based on deep learning methods and binary Harris Hawk optimization\",\"authors\":\"Noorah Jaber Faisal Jaber, Ayhan Akbas\",\"doi\":\"10.1007/s11042-024-19864-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The issue of skin cancer has garnered significant attention from the scientific community worldwide, with melanoma being the most lethal and uncommon form of the disease. Melanoma occurs due to the uncontrolled growth of melanocyte cells, which are responsible for imparting color to the skin. If left untreated, melanoma can spread throughout the body and cause death. Early detection of melanoma can lower its mortality rate. In this study, we propose a robust Convolutional Neural Network (CNN)-based method for classifying melanoma images as healthy or non-healthy. To train and test the model, we utilized public datasets from International Skin Imaging Collaboration (ISIC). Additionally, we compared our method with other classification techniques, including Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (K-NN), using the Harris Hawks Optimization algorithm. The results of our method showed superior performance compared to the other approaches.</p>\",\"PeriodicalId\":18770,\"journal\":{\"name\":\"Multimedia Tools and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multimedia Tools and Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11042-024-19864-8\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimedia Tools and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11042-024-19864-8","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

皮肤癌问题已引起全世界科学界的高度关注,其中黑色素瘤是最致命和最不常见的一种疾病。黑色素瘤是由于黑色素细胞不受控制地生长而引起的,黑色素细胞负责赋予皮肤颜色。如果不及时治疗,黑色素瘤会扩散到全身并导致死亡。及早发现黑色素瘤可以降低死亡率。在本研究中,我们提出了一种基于卷积神经网络(CNN)的鲁棒性方法,用于将黑色素瘤图像分类为健康或非健康图像。为了训练和测试该模型,我们使用了国际皮肤成像协作组织(ISIC)的公共数据集。此外,我们还利用哈里斯鹰优化算法将我们的方法与其他分类技术进行了比较,包括支持向量机(SVM)、决策树和 K-近邻(K-NN)。结果表明,与其他方法相比,我们的方法性能更优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Melanoma skin cancer detection based on deep learning methods and binary Harris Hawk optimization

Melanoma skin cancer detection based on deep learning methods and binary Harris Hawk optimization

The issue of skin cancer has garnered significant attention from the scientific community worldwide, with melanoma being the most lethal and uncommon form of the disease. Melanoma occurs due to the uncontrolled growth of melanocyte cells, which are responsible for imparting color to the skin. If left untreated, melanoma can spread throughout the body and cause death. Early detection of melanoma can lower its mortality rate. In this study, we propose a robust Convolutional Neural Network (CNN)-based method for classifying melanoma images as healthy or non-healthy. To train and test the model, we utilized public datasets from International Skin Imaging Collaboration (ISIC). Additionally, we compared our method with other classification techniques, including Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (K-NN), using the Harris Hawks Optimization algorithm. The results of our method showed superior performance compared to the other approaches.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
×
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:481959085
Book学术官方微信