A Review: Early Detection, Segmentation and Classification Techniques for Melanoma and Skin Cancer in Images

Vankayalapati Radhika, B. S. Chandana
{"title":"A Review: Early Detection, Segmentation and Classification Techniques for Melanoma and Skin Cancer in Images","authors":"Vankayalapati Radhika, B. S. Chandana","doi":"10.1109/ICSMDI57622.2023.00057","DOIUrl":null,"url":null,"abstract":"The rate of skin cancer has been increasing rapidly all over the world, making it one of the most deadly cancer categories. If it is not detected in its early stages, it can spread and cause metastases which would result in significant fatality rates. Skin cancer is curable if it is detected early, As a result, an important goal of current research is to prompt and precise detection of such malignancies. In the computer-aided diagnosis of melanoma identification and malignant categorization, many technologies have been used. The effectiveness, difficulty, and dataset quality of different methods for the detection of skin cancer techniques are examined in this study. The effectiveness of skin cancer reorganization, segmentation, and categorization methods described in the literature within the last three years is investigated in this study (2020–2022). A com parative table of the works mentioned is also induded. However, skin cancer has gained recent attention as a practical and outstanding option among the suggested solutions.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSMDI57622.2023.00057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The rate of skin cancer has been increasing rapidly all over the world, making it one of the most deadly cancer categories. If it is not detected in its early stages, it can spread and cause metastases which would result in significant fatality rates. Skin cancer is curable if it is detected early, As a result, an important goal of current research is to prompt and precise detection of such malignancies. In the computer-aided diagnosis of melanoma identification and malignant categorization, many technologies have been used. The effectiveness, difficulty, and dataset quality of different methods for the detection of skin cancer techniques are examined in this study. The effectiveness of skin cancer reorganization, segmentation, and categorization methods described in the literature within the last three years is investigated in this study (2020–2022). A com parative table of the works mentioned is also induded. However, skin cancer has gained recent attention as a practical and outstanding option among the suggested solutions.
黑色素瘤和皮肤癌图像的早期检测、分割和分类技术综述
皮肤癌的发病率在世界范围内迅速上升,使其成为最致命的癌症之一。如果在早期阶段未被发现,它可以扩散并引起转移,这将导致显着的死亡率。如果发现得早,皮肤癌是可以治愈的。因此,当前研究的一个重要目标是及时和精确地发现这种恶性肿瘤。在计算机辅助诊断黑色素瘤的识别和恶性分类中,许多技术已经被使用。在本研究中,研究了不同方法检测皮肤癌技术的有效性、难度和数据集质量。本研究(2020-2022)调查了近三年内文献中描述的皮肤癌重组、分割和分类方法的有效性。所提到的作品的比较表也包括在内。然而,皮肤癌作为建议的解决方案中一个实用而突出的选择最近引起了人们的关注。
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
自引率
0.00%
发文量
0
×
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