Covid-19 detection from X-Ray images using deep learning methods with a web app

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The World Health Organization (WHO) classified COVID-19 a global epidemic in 2019. COVID-19 is caused by SARS-CoV-2, also known as the severe acute respiratory syndrome coronavirus-2, which was found in China in late December 2019. The entire planet had been affected within a few months. COVID-19 has infected millions of individuals all over the world, making it a global health issue. The disease is usually contagious, and those who are infected can readily transfer it to others. As a result, monitoring is an effective way to stop the virus from spreading. Another condition caused by a virus linked to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is especially harmful for children, the elderly, and individuals with health problems or impaired immune systems. We employed deep transfer learning to classify COVID-19 and pneumonia in this experiment. Because there has been so much research on this subject, the suggested strategy focuses on enhancing accuracy and employs both a transfer learning methodology and a custom-made model. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. Classification accuracy was used to evaluate performance to a considerable extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. VGG-16 was 94 percent accurate in pretrained custom models, InceptionV3 was 96 percent accurate, ResNet50 threshold was 83 percent accurate, and Xception was 92.82 percent accurate. All of these models are correct, however InceptionV3 is the most accurate one for detection of Covid-19. A lightweight python framework, Flask is used to incorporate our algorithm and build our entire web application and it may be widely applicable in health sector. The information is then extracted and the results of Covid-19 detection are displayed.
TECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
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North South University
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