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The COVID-19 coronavirus is one of the devastating viruses according to theworld health organization. This novel virus leads to pneumonia, which is aninfection that inflames the lungs' air sacs of a human. One of the methods todetect those inflames is by using x-rays for the chest. In this paper, apneumonia chest x-ray detection based on generative adversarial networks (GAN)with a fine-tuned deep transfer learning for a limited dataset will bepresented. The use of GAN positively affects the proposed model robustness andmade it immune to the overfitting problem and helps in generating more imagesfrom the dataset. The dataset used in this research consists of 5863 X-rayimages with two categories: Normal and Pneumonia. This research uses only 10%of the dataset for training data and generates 90% of images using GAN to provethe efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet,Squeeznet, and Resnet18 are selected as deep transfer learning models to detectthe pneumonia from chest x-rays. Those models are selected based on their smallnumber of layers on their architectures, which will reflect in reducing thecomplexity of the models and the consumed memory and time. Using a combinationof GAN and deep transfer models proved it is efficiency according to testingaccuracy measurement. The research concludes that the Resnet18 is the mostappropriate deep transfer model according to testing accuracy measurement andachieved 99% with the other performance metrics such as precision, recall, andF1 score while using GAN as an image augmenter. Finally, a comparison resultwas carried out at the end of the research with related work which used thesame dataset except that this research used only 10% of original dataset. Thepresented work achieved a superior result than the related work in terms oftesting accuracy.

Detection of Coronavirus (COVID-19) Associated Pneumonia based on  Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model  using Chest X-ray Dataset