Lakshmi Sarvani Videla, U. Harita, Nagamani Chippada, Ch. Santhi, A. S. A. L. G. G. Gupta
{"title":"Convolution Neural Networks based COVID-19 Detection using X-ray Images of Human Chest","authors":"Lakshmi Sarvani Videla, U. Harita, Nagamani Chippada, Ch. Santhi, A. S. A. L. G. G. Gupta","doi":"10.1109/ICSSS54381.2022.9782284","DOIUrl":null,"url":null,"abstract":"In the recent times, the widespread of COVID-19 around the world has created a pandemic situation which seems to continue without an end. To handle the situation and to deal with this pandemic it is essential that the victims of COVID-19 need to be tested thoroughly so as to start effective treatment. One such test method is to take an x-ray of victim's chest and with the help of technologies like Convolution Neural Networks (CNN), the presence of COVID virus is confirmed at an early stage. With this result effective treatment can be suggested. In this paper an in-depth exploratory analysis is done to find the features that discriminate covid patient x-ray and normal patient x-ray. This paper also attempted to find the accuracy achieved by automatic feature extraction by CNN architecture. The performance of proposed CNN model is compared with the performance of existing VGG16 model.","PeriodicalId":186440,"journal":{"name":"2022 8th International Conference on Smart Structures and Systems (ICSSS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 8th International Conference on Smart Structures and Systems (ICSSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSS54381.2022.9782284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In the recent times, the widespread of COVID-19 around the world has created a pandemic situation which seems to continue without an end. To handle the situation and to deal with this pandemic it is essential that the victims of COVID-19 need to be tested thoroughly so as to start effective treatment. One such test method is to take an x-ray of victim's chest and with the help of technologies like Convolution Neural Networks (CNN), the presence of COVID virus is confirmed at an early stage. With this result effective treatment can be suggested. In this paper an in-depth exploratory analysis is done to find the features that discriminate covid patient x-ray and normal patient x-ray. This paper also attempted to find the accuracy achieved by automatic feature extraction by CNN architecture. The performance of proposed CNN model is compared with the performance of existing VGG16 model.