Acne detection, assessment, grading and classification using machine learning techniques: a review

Pooja Dhakad, S. Tiwari
{"title":"Acne detection, assessment, grading and classification using machine learning techniques: a review","authors":"Pooja Dhakad, S. Tiwari","doi":"10.26671/ijirg.2022.1.11.101","DOIUrl":null,"url":null,"abstract":"Acne is one of the most common problems faced by huge population (above 90%) at different age groups, genders and different area of acne and its severity. Among all acne types, acne vulgaris is most common of them. Acne vulgaris has become an interesting domain for researchers in biomedical engineering as well as image processing. Recognizing acne region and skin areas accurately is really challenging task.This plays a major role in acne detection, grading, classification, acne severity detection and automatic acne assessment. This paper presents a comprehensive review which aim to fill the research gap in literature by providing all the state-of-the-art methods applied till date on acne vulgaris images. This research area is least explored and hence this paper focuses on survey of various image processing and machine learning techniques applied on acne images. Future scope and the problems identified in this domain are also elaborated.","PeriodicalId":118199,"journal":{"name":"International Journal of Innovative Research and Growth","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Innovative Research and Growth","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26671/ijirg.2022.1.11.101","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

Acne is one of the most common problems faced by huge population (above 90%) at different age groups, genders and different area of acne and its severity. Among all acne types, acne vulgaris is most common of them. Acne vulgaris has become an interesting domain for researchers in biomedical engineering as well as image processing. Recognizing acne region and skin areas accurately is really challenging task.This plays a major role in acne detection, grading, classification, acne severity detection and automatic acne assessment. This paper presents a comprehensive review which aim to fill the research gap in literature by providing all the state-of-the-art methods applied till date on acne vulgaris images. This research area is least explored and hence this paper focuses on survey of various image processing and machine learning techniques applied on acne images. Future scope and the problems identified in this domain are also elaborated.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用机器学习技术检测、评估、分级和分类痤疮:综述
痤疮是广大人群(90%以上)在不同年龄组、性别和不同痤疮部位及其严重程度所面临的最常见问题之一。在所有类型的痤疮中,寻常性痤疮是最常见的。寻常痤疮已成为生物医学工程和图像处理研究人员感兴趣的领域。准确识别痤疮区域和皮肤区域确实是一项具有挑战性的任务。这在痤疮检测、分级、分类、痤疮严重程度检测和痤疮自动评估中起着重要作用。本文提出了一个全面的审查,其目的是填补研究空白,在文献提供所有的国家的最先进的方法应用到寻常痤疮图像的日期。这一研究领域是探索最少的,因此本文着重于调查各种图像处理和机器学习技术在痤疮图像上的应用。未来的范围和问题确定在这一领域也进行了阐述。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Assessment of Micronutrient Deficiencies and their Impact on Maternal Health in Secunderabad Analyzing Global Terrorism Database for Identification of Terrorist Group Geomagnetic Storms in Relation to Magnetic Clouds, Hard X-Ray Solar Flares, and Disturbances in Interplanetary Magnetic Fields During 1996-2008 The generalized K-function and its recurrence relations Fractional calculus and generalized K-function
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1