Automated Detection of Covid-19 Coronavirus Cases Using Deep Neural Networks with CT Images | ||||
| Al-Azhar University Journal of Virus Researches and Studies | ||||
| Article 1, Volume 2, Issue 1, 2020, Page 1-11 PDF (780.15 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/AUJV.2020.106728 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Laila M. Aboughazala | ||||
| Center for Virus Research and Studies, Al-Azhar University, Cairo, Egypt | ||||
| Abstract | ||||
| Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 746 CT volumes of 359 patients with confirmed COVID-19 and 387 negative cases from publicly available chest CT datasets. In this paper, we propose a deep learning architecture to detect Covid-19 Coronavirus in CT Images. This architecture contains one network to classify images as either Covid-19 or Non-Covid-19. The experiment results evaluated by three parameters including accuracy, sensitivity, and specificity. For the ResNet-50 deep learning, these outcomes refer to the maximum sensitivity being 91.69% by the training dataset for the ResNet-50. ResNet-50 can be considered as a high sensitivity model to characterize and diagnose Covid-19 Coronavirus and can be used as an adjuvant tool in radiology departments. | ||||
| Keywords | ||||
| COVID-19; CT images; Deep learning; ResNet-50 architecture | ||||
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