An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network

(E-pub Abstract Ahead of Print)

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

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

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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 the 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 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 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 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 & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

Keywords: Coronavirus, covid-19, chest x-ray radiographs, real time – polymerase chain reaction, VGG19, convolutional neural network.

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Article Details

(E-pub Abstract Ahead of Print)
DOI: 10.2174/1573405617999210112193220
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