An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network

Title:An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network

Volume: 18

Author(s): P Srinivasa Rao*, Pradeep Bheemavarapu, P S Latha Kalyampudi and T V Madhusudhana Rao

Affiliation:

• Department of CSE, MVGR College of Engineering(A). Vizianagaram, A.P. India-535005

Keywords: Coronavirus, SARS-COV-2, chest x-ray radiographs, real-time – polymerase chain reaction, VGG19, convolutional neural network.

Abstract:

Background: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population.

Objective: This paper proposes a deep learning model for the classification of coronavirus infected patient detection using chest X-ray radiographs.

Methods: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with the rectified linear unit, softmax (last layer) activation functions, and max-pooling layers which were trained using the publicly available COVID-19 dataset.

Results and Conclusion: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting of COVID-19 and normal patient’s images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE and accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

Cite this article as:

Rao Srinivasa P*, Bheemavarapu Pradeep , Kalyampudi Latha P S and Rao Madhusudhana T V, An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network, Current Medical Imaging 2022; 18 (6) : e100122190239 . https://dx.doi.org/10.2174/1573405617999210112193220

DOI https://dx.doi.org/10.2174/1573405617999210112193220	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603