Automated Detection of Covid-19 Coronavirus Cases Using Deep Neural Networks with X-ray Images | ||||
| Al-Azhar University Journal of Virus Researches and Studies | ||||
| Article 1, Volume 2, Issue 1, 2020, Page 1-12 PDF (617.4 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/AUJV.2020.106722 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Laila M. Aboughazala | ||||
| Center for Virus Research and Studies, Al-Azhar University, Cairo, Egypt | ||||
| Abstract | ||||
| Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. In this paper, we propose a deep learning architecture to detect Covid-19 Coronavirus in chest radiographs. This architecture contains one network to classify images as either normal or Covid-19 Coronavirus. In this paper, we adopt ResNet-50 architecture in this blog as it has proven to be highly effective for various medical imaging applications. Those X-ray images database contain 147 images. The data divided into 73 samples for the normal X-ray images and 74 samples for Covid-19 Coronavirus X-ray images. The experiment results evaluated by three parameters including accuracy, sensitivity, and specificity. For the ResNet-50 deep learning, these outcomes refer to the maximum accuracy being 99.3% by the training dataset for the ResNet-50. ResNet-50 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections and can be used as an adjuvant tool in radiology departments. | ||||
| Keywords | ||||
| COVID-19; X-ray images; Deep learning; ResNet-50 architecture | ||||
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