Coronavirus Detection using Deep Pre-trained Model on Chest X-ray Imaging

Abstract

The constant increase in the number of patients suffering from the pandemic Covid-19 has overpowered the medical management system over the world. This pandemic has affected every aspect of human life all over the world. However, the limited number of testing kits is making it more challenging to deal with the global pandemic. It is near to impossible for people to maintain social distancing all the time, and lengthy testing procedure is one of the major cause of the rapid spread of the epidemic Covid-19. The polymerase chain reaction test that is done in real-time (RT-PCR) is time-consuming for each patient with gasping disease to be tested. However, the use of a chest x-ray machine tool can accelerate testing time. X-ray machines are now accessible in almost every medical management healthcare system, with instant results. The proposed research aims to detect the pandemic. The dataset was collected from an open-source repository that consists of the chest x-ray images of patients who were Covid-19 positive, and covid-19 negative which means not infected by the pandemic coronavirus but suffering from normal pneumonia. The approach of this paper uses a deep convolutional neural network model i-e VGG16, to begin the process of classifying images into positive or negative. In the proposed research VGG16, the Deep Convolutional Network model is used, this is a pre-trained model, previously trained on the ImageNet dataset that com-prises 14M images that belong to 1K different classes. This model is fine-tuned on 1202 images. The preprocessing of collected data is done by converting it into an RGB channel and resizing it to match the requirement of our model i-e VGG16. Data augmentation is performed on the preprocessed dataset. Our proposed model gives 99.1%, 100%, and 98.59% accuracies, sensitivity specificity respectively for the detection of Covid-19 on patients’ cxr images.