Article: COVID-19 detection through convolutional neural networks and chest X-ray images Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2022 Vol.14 No.4 pp.336 - 346 Abstract: To break the chain of COVID-19, a powerful and fast screening system is required which identifies the COVID-19 affected cases quickly such that the appropriate measures like quarantine or treatment can be taken. The traditional RT-PCR test is found to have larger misclassification rate. It also consumes more time to get the result. To solve this problem, in this paper, we have introduced a new model for COVID-19 detection based on chest X-ray (CXR) images and convolutional neural networks (CNNs). The proposed model is an automatic detection model, which considers the CXR image as input and performs an in-depth analysis to discover the COVID-19. The proposed CNN model is a very simple and effective, which is composed of five convolutional layers and three pooling layers. Every convolutional layer has different sized filters and different number of filters, which extracts all the possible features from CXR image. Simulation experiments are conducted over a newly constructed dataset based on the publicly available CXR (both COVID-19 and non-COVID-19) images. Simulation is done under two phases; 3-class and 2-class and obtained an average accuracy of 92.22% and 94.44% respectively. Thus, the average accuracy is measured as 93.33%. Inderscience Publishers - linking academia, business and industry through research

Title: COVID-19 detection through convolutional neural networks and chest X-ray images

Authors: K. Venkata Subbareddy; L. Nirmala Devi

Addresses: Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India ' Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India

Abstract: To break the chain of COVID-19, a powerful and fast screening system is required which identifies the COVID-19 affected cases quickly such that the appropriate measures like quarantine or treatment can be taken. The traditional RT-PCR test is found to have larger misclassification rate. It also consumes more time to get the result. To solve this problem, in this paper, we have introduced a new model for COVID-19 detection based on chest X-ray (CXR) images and convolutional neural networks (CNNs). The proposed model is an automatic detection model, which considers the CXR image as input and performs an in-depth analysis to discover the COVID-19. The proposed CNN model is a very simple and effective, which is composed of five convolutional layers and three pooling layers. Every convolutional layer has different sized filters and different number of filters, which extracts all the possible features from CXR image. Simulation experiments are conducted over a newly constructed dataset based on the publicly available CXR (both COVID-19 and non-COVID-19) images. Simulation is done under two phases; 3-class and 2-class and obtained an average accuracy of 92.22% and 94.44% respectively. Thus, the average accuracy is measured as 93.33%.

Keywords: COVID-19; deep learning; convolutional neural network; CNN; CXR images; accuracy.

DOI: 10.1504/IJMEI.2022.123927

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.4, pp.336 - 346

Received: 16 May 2020
Accepted: 15 Sep 2020
Published online: 05 Jul 2022 *

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Title: COVID-19 detection through convolutional neural networks and chest X-ray images

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