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ORIGINAL ARTICLE
Year : 2021  |  Volume : 5  |  Issue : 1  |  Page : 43-49

Modified VGG deep-learning architecture for COVID-19 classification using chest radiography images


1 Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore; Department of ECE, Sona College of Technology, Salem, Tamil Nadu, India
2 Center for Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

Correspondence Address:
R Anand
Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham & Department of ECE, Sona College of Technology, Salem, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bbrj.bbrj_143_20

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Background: The world faced a deadly disease encounter by the starting of 2020, known as coronavirus disease 2019 (COVID-19). Due to the rapid increase in the counts of COVID cases, the WHO declared the COVID-19 as a pandemic on March 11, 2020. Among the different screening techniques available for COVID-19, radiography of the chest is one of the efficient way for disease detection. While other disease detection techniques take time, radiography takes less time to identify because of the abnormalities caused by the disease in the lungs. Methods: In the rapid development era of artificial intelligence and deep-learning techniques, various models are being developed for COVID disease detection. COVID-19 can be easily detected from Chest X-ray images and the pretrained models yield high accuracy with small dataset. Results: In this paper, one of the standard deep-learning architectures, VGGNet, is modified for classifying chest X-ray images under four categories. The proposed model uses open source dataset that contains 231, 2503, 1345, and 1341 images of four classes such as COVID, bacterial, normal, and viral chest radiography images, respectively. Conclusion: The performance matrices of the proposed work were compared with the five benchmark deep-learning architectures namely VGGNet, AlexNet, GoogLeNET, Inception-v4, and DenseNet-201.


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