COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
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COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images Alaa S. Al-Waisy1 • Shumoos Al-Fahdawi2 Salama A. Mostafa5 • Mashael S. Maashi6
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Mazin Abed Mohammed3 • Karrar Hameed Abdulkareem4 Muhammad Arif7 • Begonya Garcia-Zapirain8
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Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result. Keywords Coronavirus COVID-19 epidemic Deep learning Transfer learning ResNet34 model Chest radiography imaging Chest X-ray images
Communicated by Valentina E. Balas. & Alaa S. Al-Waisy [email protected] Shumoos Al-Fahdawi [email protected] Mazin Abed Mohammed [email protected] Karrar Hameed Abdulkareem [email protected] Salama A. Mostafa [email protected] Mashael S. Maashi [email protected] Muhammad Arif [email protected] Begonya Garcia-Zapirain [email protected] Extended author information available on the last page of the article
1 Introduction The Coronavirus (COVID-19) epidemic is one of the most infectious diseases, which is distinguished as a pandemic due to its ability to rapidly spread in most of the world countries with serious effects on the lives of billions of people. The first COVID-19 infected case was identified in December 2019 in Wuhan city. Recently, all the countries arou
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