Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Abstract

Abstract Background: The outbreak of COVID-19 on the eve of January 2020 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) in mid-March. Currently the outbreak has affected more than 150 countries with more than 20 million confirmed cases and more than 700,000 death tolls. The standard method for detection of COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) which is less sensitive, expensive and required specialized health expert. As the number of cases continue to grow, there is high need for developing rapid screening method that is accurate, fast and cheap. Methods : We proposed the use of Deep Learning approach based on Pretrained AlexNet Model for classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia and normal Chest X-rays Images (CXR) scans obtained from different public databases. Result and Conclusion : For non-COVID-19 viral pneumonia and healthy datasets, the model achieved 94.43% accuracy, 98.19% Sensitivity and 95.78% Specificity. For bacterial pneumonia and healthy datasets, the model achieved 91.43% accuracy, 91.94% sensitivity and 100% Specificity. For COVID-19 pneumonia and healthy CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity and 100% Specificity. For classification of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity and 99.89% Specificity. For multiclass datasets the model achieved 94.00% accuracy, 91.30% sensitivity and 84.78% specificity for COVID-19, bacterial pneumonia and healthy. For 4 classes (COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia and healthy, the model achieved accuracy of 93.42%, sensitivity of 89.18% and specificity of 98.92%.

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