COVID-19 diagnosis using Transfer Learning on Xray Images

Abstract

Deadly corona virus disease has an effect on people's everyday lives condition and the economy of a country. Diagnose of COVID-19 in the patient in most of the laboratories used real-time reverse transcription polymerase chain reaction
(RT-PCR) technique. Initially disease, performance of RT-PCR is not up to the mark due to time required for the diagnosis and high false positives and false negatives outcomes. The diagnosis of this particular disease from radiography imageries is the fastest method. The imaging technique is thought to be a quick diagnosis mechanism to rapidly identify suspicious patients in an epidemic area. We have suggested and developed an automatic system for the detection of COVID-19 samples from normal and pneumonia cases by chest x-ray. We have combined 5 publicly available datasets which include COVID-19 from Kaggle, Mendley, SIRM and NIH images comprising Healthy, Pneumonia and infected patients. Numerous pre-trained transfer learning models namely Resnet50, VGG19, VGG16, MobileNetV2, InceptionResNetV2, EfficientNetB0 and ResNet Mobile have been used for disease diagnosis by utilizing the chest x-rays. A total of 3000 images with class balanced dataset are used to determine the performance suggested method. We have also compared the performance of seven pre-trained transfer learning algorithms to help identify COVID-19 detection efficiency of transfer learning methods for diagnosis using chest x-ray. Resnet50 shows highest classification accuracy of 97%.