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Title |
A Deep Learning Approach for Covid-19 Detection in Chest X-Rays |
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Author |
Sk. Shalauddin Kabir, Syed Galib, Hazrat Ali , Fee Faysal Ahmed, Mohammad Farhad Bulbul |
| Citation |
Vol. 24 No. 3 pp. 125-134 |
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Abstract |
The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19. |
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Keywords |
Convolutional Neural Networks, X-ray, COVID19, Transfer-Learning, Deep-Learning |
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