Previously submitted to: JMIR Medical Informatics (no longer under consideration since Aug 24, 2020)
Date Submitted: Jun 14, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
An Efficient Approach to Predict COVID-19 Infected Patient Applying Deep learning Algorithm on Chest X-ray Images with Analyzing the Patient Symptoms
ABSTRACT
Background:
COVID-19 is a life-threatening infectious disease that has become a pandemic for the time being. The virus grows within the lower respiratory tract where early-stage symptoms(like- cough, fever, sore throat, etc.) develop and then it causes lung infection(pneumonia)
Objective:
This paper proposed a new methodology of artificial testing whether a patient has been infected by COVID-19 or not
Methods:
We have presented a prediction model based on, Convolutional Neural Networks(CNN) and our own developed mathematical equation based algorithm named SymptomNet. The CNN algorithm classifies the lung infection(pneumonia) from frontal chest X-ray images, while the symptoms analyzing algorithm(SymptomNet) predicts the possibility of COVID-19 infection from developed symptoms in a patient
Results:
The model has the accuracy of 96% while predicting COVID-19 patients. In this Model, the CNN classifier has the accuracy of around 96% and the SymptomNet algorithm has the accuracy of 97%.
Conclusions:
This research work obtained a promising accuracy while predicting COVID-19 infected patients. The proposed model can be ubiquitously used at a low cost with high accuracy.
Citation
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Copyright
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