Article: A deep neural network-based architecture for automated detection of COVID-19 from chest X-ray images Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2024 Vol.16 No.2 pp.173 - 184 Abstract: The COVID-19 pandemic has a severe adverse impact on people's health, lives, and the worldwide global economy. It is only possible to identify positive COVID-19 instances if each country carries out sufficient tests. As suggested in prior studies, X-ray images could be used as testing samples to develop a reliable and low-cost COVID-19 testing model. This paper introduces a deep learning-based end-to-end binary classification framework, Att-Net, for automated detection of COVID-19 cases using chest X-ray images. In this work, we have adopted pre-trained ConvNet (VGG-16) with an attention module embedded with the VGG-16 architecture, which significantly improves the model's performance. The proposed architecture is evaluated on the COVID-Xray-5k dataset. The suggested methodology obtains a state-of-the-art sensitivity of 98.5% and specificity of 99.4%. This work also presents a detailed performance analysis in terms of accuracy, sensitivity, specificity, precision, recall, and F-score. Furthermore, we have also generated the heat maps, which reveal the most anticipated regions infected by COVID-19 while learning for prediction by the CNN to validate the proposed architecture. Inderscience Publishers - linking academia, business and industry through research

Title: A deep neural network-based architecture for automated detection of COVID-19 from chest X-ray images

Authors: Abul Abbas Barbhuiya; Ram Kumar Karsh; Rahul Jain; Hillol Phukan

Addresses: Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam 788010, India ' Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam 788010, India ' Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam 788010, India ' Department of Electrical Engineering, National Institute of Technology Silchar, Assam 788010, India

Abstract: The COVID-19 pandemic has a severe adverse impact on people's health, lives, and the worldwide global economy. It is only possible to identify positive COVID-19 instances if each country carries out sufficient tests. As suggested in prior studies, X-ray images could be used as testing samples to develop a reliable and low-cost COVID-19 testing model. This paper introduces a deep learning-based end-to-end binary classification framework, Att-Net, for automated detection of COVID-19 cases using chest X-ray images. In this work, we have adopted pre-trained ConvNet (VGG-16) with an attention module embedded with the VGG-16 architecture, which significantly improves the model's performance. The proposed architecture is evaluated on the COVID-Xray-5k dataset. The suggested methodology obtains a state-of-the-art sensitivity of 98.5% and specificity of 99.4%. This work also presents a detailed performance analysis in terms of accuracy, sensitivity, specificity, precision, recall, and F-score. Furthermore, we have also generated the heat maps, which reveal the most anticipated regions infected by COVID-19 while learning for prediction by the CNN to validate the proposed architecture.

Keywords: COVID-19; pandemic; deep learning; ConvNet; machine learning; transfer learning; feature extraction; X-ray images; healthcare management; CNN.

DOI: 10.1504/IJMEI.2024.136964

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.2, pp.173 - 184

Received: 20 Jul 2021
Accepted: 11 Jan 2022
Published online: 01 Mar 2024 *

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Title: A deep neural network-based architecture for automated detection of COVID-19 from chest X-ray images

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