Pneumonia Classification in X-ray Images Using Artificial Intelligence Technology

Han Trong Thanh, P. H. Yen, Trinh Bich Ngoc
{"title":"Pneumonia Classification in X-ray Images Using Artificial Intelligence Technology","authors":"Han Trong Thanh, P. H. Yen, Trinh Bich Ngoc","doi":"10.1109/ATiGB50996.2021.9423017","DOIUrl":null,"url":null,"abstract":"The article focuses on the research of image classification algorithms, namely the images indicate pathology of pneumonia caused by bacteria and viruses. The proposed method is based on using the VGG16, VGG19, DenseNet169 networks to extract data characteristics and train the model classification. The X-rays are classified including normal people, patients with viral pneumonia, and bacterial pneumonia. The provided source was medical data on chest X- ray images of patients who were manually classified by specialists. However, the accuracy of the classification is highly dependent on the number of images, the resolution of the images, and whether the X-ray image is correctly classified. In this study, the algorithms give relatively positive classification results with an accuracy of approximately 85%.","PeriodicalId":6690,"journal":{"name":"2020 Applying New Technology in Green Buildings (ATiGB)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Applying New Technology in Green Buildings (ATiGB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATiGB50996.2021.9423017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

The article focuses on the research of image classification algorithms, namely the images indicate pathology of pneumonia caused by bacteria and viruses. The proposed method is based on using the VGG16, VGG19, DenseNet169 networks to extract data characteristics and train the model classification. The X-rays are classified including normal people, patients with viral pneumonia, and bacterial pneumonia. The provided source was medical data on chest X- ray images of patients who were manually classified by specialists. However, the accuracy of the classification is highly dependent on the number of images, the resolution of the images, and whether the X-ray image is correctly classified. In this study, the algorithms give relatively positive classification results with an accuracy of approximately 85%.
利用人工智能技术在x射线图像中的肺炎分类
本文重点研究图像分类算法,即图像显示细菌和病毒引起的肺炎的病理。该方法基于VGG16、VGG19、DenseNet169网络提取数据特征并进行模型分类训练。x光片分为正常人、病毒性肺炎和细菌性肺炎。提供的来源是由专家手动分类的患者胸部X线图像的医学数据。然而,分类的准确性高度依赖于图像的数量、图像的分辨率以及x射线图像是否被正确分类。在本研究中,这些算法给出了相对积极的分类结果,准确率约为85%。
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