Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.
This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PC...
| Published in: | International Journal of Biomedical Imaging pp. 1 - 16 |
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| Main Authors: | , |
| Format: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
12/22/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160939803&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160939803 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16874188 1WZI jtl: International Journal of Biomedical Imaging issn: 16874188 maglogo: N pubinfo: dt: 12/22/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 160939803 160939803 160939803 10.1155/2022/5318447 160939803 ppf: 1 ppct: 15 formats: tig: atl: Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques. aug: au: Islam, Rumana Tarique, Mohammed affil: Department of ECE, University of Windsor, ON, Canada, N9B 3P4 sug: subj: Radiography, Thoracic COVID-19 Diagnosis Pneumonia Diagnosis Artificial Intelligence Radiographic Image Interpretation, Computer-Assisted Human Reverse Transcriptase Polymerase Chain Reaction Algorithms Neural Networks (Computer) Descriptive Statistics United Arab Emirates ab: This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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