COVID-19 Pneumonia Detection in Chest X-ray Images Using Transfer Learning of Convolutional Neural Networks

Abstract

The COVID-19 pandemic is the defining global health crisis and the greatest challenge. Since its emergence and has spread rising daily worldwide. The early diagnosis of COVID-19 can improve a chance of survival and can support clinical treatment. An automatically COVID-19 pneumonia detection will support the medical diagnosis to examine the chest X-ray image. For this reason, this work intent to develop the CNN model by the process of transfer learning. The models will be created from the three pre-trained models include Xception, VGG16, and Inception-Resnet-V2 model. Then, these newly proposed models will be applied to detect COVID-19 pneumonia from the X-ray image dataset. The proposed models enhance to diagnose the chest X-ray images of patients who have pneumonia by COVID-19. Since the existing COVID-19 X-ray dataset has a small number of images containing 323 images, this work uses the data augmentation techniques to increase the virtual number of images. The results reveal that the proposed models have performed the classification task for detecting pneumonia. The proposed model achieved an accuracy level of Xception, VGG16, and Inception-Resnet-V2 is 97.19%, 95.42% and 93.87%, respectively. It reveals that the CNN model with the Xception has higher accuracy than VGG16 and Inception-ResNet-V2 model.