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CovidXplus-A New Mobile Application for Image-Guided Diagnosis of COVID-19 Patients


  • Mohamed Esmail Karar College of Computing and Information Technology (CCIT), Shaqra University, Saudi Arabia
  • Bilal Ahmad College of Computing and Information Technology (CCIT), Shaqra University, Saudi Arabia



Patients with unexplained pneumonia were discovered in Wuhan City, China, at the end of 2019, according to the World Health Organization (WHO). Chinese authorities announced on January 2020 that they discovered a new virus that causes these infections. That's why the virus was assigned the name of novel Coronavirus Disease 2019 (COVID-19). It is a new disease that affects the lungs and airways and can cause mild to severe illness, as well as pneumonia. Coronaviruses are a wide family of viruses that can infect both animals and humans. Extreme Acute Respiratory Syndrome 2 is the most recent Coronavirus to be discovered (SARS-CoV-2). According to the results, the virus spreads from person to person in close contact over a distance of about 2 meters. When someone coughs or sneezes, respiratory droplets are released, which spread the virus. As a result, we need to build an application that allows use of computer-aided diagnosis (CAD) systems for detection of the COVID-19 based on radiological techniques. This paper proposes a novel mobile application based on fine-tuned transfer learning models to boost the efficiency of CAD systems in the detection of the highly suspected COVID-19 patients using medical X-ray images. Three fine-tuned deep learning models, namely ResNet50, ResNet101, and ResNet152 are exploited in this study.


COVID-19, Computer-Aided Diagnosis, Deep Learning, Internet of Medical Things


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Author Biography

Mohamed Esmail Karar, College of Computing and Information Technology (CCIT), Shaqra University, Saudi Arabia




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