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Classification of COVID-19 and Influenza Patients Using Deep Learning.
Aftab, Muhammad; Amin, Rashid; Koundal, Deepika; Aldabbas, Hamza; Alouffi, Bader; Iqbal, Zeshan.
  • Aftab M; Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
  • Amin R; Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
  • Koundal D; Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
  • Aldabbas H; Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan.
  • Alouffi B; Taif University, College of Computer and IT, Taif, Saudi Arabia.
  • Iqbal Z; Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Contrast Media Mol Imaging ; 2022: 8549707, 2022.
Article in English | MEDLINE | ID: covidwho-2248150
ABSTRACT
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Tomography, X-Ray Computed / Influenza, Human / Deep Learning / SARS-CoV-2 / COVID-19 Type of study: Experimental Studies Topics: Long Covid Limits: Female / Humans / Male Language: English Journal: Contrast Media Mol Imaging Journal subject: Diagnostic Imaging Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Tomography, X-Ray Computed / Influenza, Human / Deep Learning / SARS-CoV-2 / COVID-19 Type of study: Experimental Studies Topics: Long Covid Limits: Female / Humans / Male Language: English Journal: Contrast Media Mol Imaging Journal subject: Diagnostic Imaging Year: 2022 Document Type: Article Affiliation country: 2022