A new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve accuracy

Madderi Sivalingam Saravanan, J. C. Antony, V. P. Kumar, M. Veeramanickam, MAKARAND UPADHYAYA
{"title":"A new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve accuracy","authors":"Madderi Sivalingam Saravanan, J. C. Antony, V. P. Kumar, M. Veeramanickam, MAKARAND UPADHYAYA","doi":"10.1109/ICAITPR51569.2022.9844216","DOIUrl":null,"url":null,"abstract":"Cervical cancer is not new to the research at the same time it has more impact on society to motivate to find better solutions to predict at the earlier stage to avoid the severity of the patient. Cervical cancer has various stages of severity and it will be analyzed using various diagnosis methods. Therefore to identify the severity, this research study will use machine learning approaches to analyze the medical diagnosis inputs. In this study, the cancerous pap smear images are given as input on machine learning algorithms and predicted the severity of decease for better treatment. Therefore this research paper proposes a new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve the better accuracy of the existing research studies.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAITPR51569.2022.9844216","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Cervical cancer is not new to the research at the same time it has more impact on society to motivate to find better solutions to predict at the earlier stage to avoid the severity of the patient. Cervical cancer has various stages of severity and it will be analyzed using various diagnosis methods. Therefore to identify the severity, this research study will use machine learning approaches to analyze the medical diagnosis inputs. In this study, the cancerous pap smear images are given as input on machine learning algorithms and predicted the severity of decease for better treatment. Therefore this research paper proposes a new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve the better accuracy of the existing research studies.
一个新的框架,分类癌性和非癌性巴氏涂片图像使用滤波技术,以提高准确性
宫颈癌对研究来说并不新鲜,同时它对社会的影响更大,可以激励人们找到更好的解决方案,在早期阶段进行预测,以避免患者的严重程度。子宫颈癌有不同的严重程度阶段,将使用不同的诊断方法进行分析。因此,为了识别严重程度,本研究将使用机器学习方法来分析医学诊断输入。在这项研究中,癌性巴氏涂片图像作为机器学习算法的输入,并预测疾病的严重程度,以便更好地治疗。因此,本文提出了一种新的框架,利用滤波技术对癌性和非癌性巴氏涂片图像进行分类,以提高现有研究的准确性。
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:604180095
Book学术官方微信