Article ; Online: Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework
medRxiv
Abstract: The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient9s treatment is the faster and accurate detection of the disease which is ...
| Abstract | The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient9s treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, the implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00, and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection. |
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| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-11-12 |
| Publisher | Cold Spring Harbor Laboratory Press |
| Document type | Article ; Online |
| DOI | 10.1101/2020.11.08.20227819 |
| Database | COVID19 |
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