Deep-COVID-19: Deep Learning for COVID 19 Detection from X-ray Images
Ahmed Hashem El Fiky

Ahmed Hashem El Fiky*, Department of Systems and Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.

Manuscript received on November 06, 2021. | Revised Manuscript received on November 10, 2021. | Manuscript published on November 30, 2021. | PP: 1-6 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95891111121 | DOI: 10.35940/ijitee.A9589.1111121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy.
Keywords: Coronavirus; Convolution Neural Networks; Deep-Learning; Covid-19.