Classification of COVID-19 and Pneumonia X-ray Images Using a Transfer Learning Approach

The Coronavirus disease is a respiratory infection caused by the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), also known colloquially as COVID-19 virus. Non-Covid Viral Pneumonia is also a respiratory disease which affects the lungs of the patients. It becomes difficult for radiologists and pulmonologists to differentiate between these two respiratory diseases. Chest X-ray images of the patients can be used to efficiently diagnose between these two respiratory diseases. Deep learning models can be efficiently used to detect subtle differences between these X-ray images. This method can be used to gain faster results in COVID-19 cases thereby reducing the time taken for identifying a COVID-19 patient. X- ray images are cheaper and faster than the currently existing methods. A Transfer Learning approach is adopted to classify chest X-rays into three categories such as COVID-19, Pneumonia and Normal. Popular ImageNet Architectures: VGG-19, MobileNet and ResNet-50 are used to classify X-ray images of the patients. From the experimental results, it is evident that the MobileNet is able to achieve a validation accuracy of 0.9777 in 40 epochs.