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Dr Mashael Suliaman Maashi (BSc, MSc, PhD) دكتورة مشاعل بنت سليمان معشي

Associate Professor

Faculty, Director of the Research Center

علوم الحاسب والمعلومات
Building# 6, floor# 3, Office No#69
المنشورات
مقال فى مجلة
2020

COVID-CheXNet: Hybrid Deep Learning Framework for Identifying COVID-19 Virus in Chest X-rays Images

A.,, Al-Waisy, . 2020

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 diagnosingCOVID-
19 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.

مجلة/صحيفة
Soft Computing
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