基于计算机断层扫描图像的Covid-19分类的自监督对比学习

K. Mohit, Rajeev Gupta, B. Kumar
{"title":"基于计算机断层扫描图像的Covid-19分类的自监督对比学习","authors":"K. Mohit, Rajeev Gupta, B. Kumar","doi":"10.1109/UPCON56432.2022.9986406","DOIUrl":null,"url":null,"abstract":"Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images\",\"authors\":\"K. Mohit, Rajeev Gupta, B. Kumar\",\"doi\":\"10.1109/UPCON56432.2022.9986406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time.\",\"PeriodicalId\":185782,\"journal\":{\"name\":\"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UPCON56432.2022.9986406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UPCON56432.2022.9986406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

在2019冠状病毒病大流行爆发期间,计算机辅助诊断(CAD)成为一种详尽的诊断工具,并被广泛研究以实现更准确的自动检测。基于人工智能(AI)的放射图像(计算机断层扫描、x射线、肺部超声)解读提高了Covid-19感染的整体诊断效率。本文讨论了基于CAD的深度元学习方法,用于自动快速分析胸部计算机断层扫描(CT)图像,以早期检测受试者体内的冠状病毒(Covid-19)。我们采用自监督对比学习神经网络对28203张胸部CT图像进行无偏特征表示和分类,使用微调的预训练Inception模块。该可训练的多镜头端到端深度学习架构在normal和covid-19 CT图像的公共数据集上进行了验证,归一化精度为0.9708。结果验证了我们的模型能够在更短的时间内帮助放射科医生和专家筛查和正确诊断Covid-19患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images
Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time.
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
自引率
0.00%
发文量
0
期刊最新文献
Mains Interface Circuit Design for Traveling Wave Tube Amplifier A Passive Technique for Detecting Islanding Using Voltage Sequence Component A Unified Framework for Covariance Adaptation with Multiple Source Domains Advance Sensor for Monitoring Electrolyte Leakage in Lithium-ion Batteries for Electric Vehicles A comparative study of survey papers based on energy efficient, coverage-aware, and fault tolerant in static sink node of WSN
×
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