Working Paper Article Version 1 This version is not peer-reviewed

COVID19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?

Version 1 : Received: 12 June 2020 / Approved: 14 June 2020 / Online: 14 June 2020 (17:51:12 CEST)

How to cite: Saqib, M.; Anwar, S.; Anwar, A.; Petersson, L.; Sharma, N.; Blumenstein, M. COVID19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?. Preprints 2020, 2020060189 Saqib, M.; Anwar, S.; Anwar, A.; Petersson, L.; Sharma, N.; Blumenstein, M. COVID19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?. Preprints 2020, 2020060189

Abstract

The COVID-19 is a highly contagious viral infection which played havoc on everyone's life in many different ways. According to the world health organization and scientists, more testing potentially helps governments and disease control organizations in containing the spread of the virus. The use of chest radiographs is one of the early screening tests to determine the onset of disease, as the infection affects the lungs severely. This study will investigate and automate the process of testing by using state-of-the-art CNN classifiers to detect the COVID19 infection. However, the viral could of many different types; therefore, we only regard for COVID19 while the other viral infection types are treated as non-COVID19 in the radiographs of various viral infections. The classification task is challenging due to the limited number of scans available for COVID19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately. All trained models are available at https://github.com/saeed-anwar/COVID19-Baselines

Subject Areas

COVID-19; Deep learning; Convolutional neural network; Computed Tomography; X-Rays; Classification; Detection

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