Accurate Diagnosis of COVID-19 Based on Deep Neural Networks and Chest X-Ray Images | ||
| MEJ- Mansoura Engineering Journal | ||
| Articles in Press, Accepted Manuscript, Available Online from 21 July 2020 | ||
| Document Type: Review articles | ||
| DOI: 10.21608/bfemu.2020.30471.1001 | ||
| Authors | ||
| Hossam El-Din Moustafa* 1; Mervat El-Seddek2 | ||
| 1Mansoura University Faculty of Engineering Electronics and Communications Department | ||
| 2Department of Electronics and Communications - Misr Higher Institute for Engineering and Technology - Mansoura | ||
| Abstract | ||
| The present study aims at preventing spread out of COVID-19 by early detection of infected cases using chest X-ray images and convolutional neural networks. Covid-19 chest X-ray dataset were collected from public sources as well as through agreements with hospitals and physicians with the consent of their patients. A deep learning algorithm based on convolutional neural networks (CNN) was implemented utilizing X-ray images to diagnose COVID-19. ResNet50, short for Residual Networks, is a classic neural network that was used as a backbone for the classification task. It accelerates the speed of training of the deep networks and reduces the effect of vanishing gradient problems. Images were first resized and then pre-processed to increase sharpness, contrast, and clarity. Images were fed into a deep neural network to predict the probability of COVID-19 infectious. The deep learning calculation acquired an area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.9888, 96.2% sensitivity, 98% accuracy, and 100% specificity. Moreover, the algorithm can be easily modified to add extra images (normal and COVID-19) to improve performance. The proposed system introduces a great help to all nations to screen and diagnose COVID-19 as a faster alternative compared with conventional method that uses PCR. | ||
| Keywords | ||
| COVID-19; Chest X-ray Images; Deep Learning, CNN | ||
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