In recent years, deep learning techniques have been widely used to diagnose diseases. However, in the diagnosis of Covid-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. To address this, data from several different sources can be combined using transfer learning. technique. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model. In this method, the network, while trying to classify the data correctly, tries to make the representations of the source and target datasets as similar as possible to achieve better results in terms of quantity and quality for both datasets. we also use the center loss function to train the model. Using the center loss function helps to better distinguish classes from each other. We show that accuracy can be improved using the proposed framework, and surpass the results of current successful transfer learning approaches. The proposed method has achieved 2, 15, 15, and 8% improvement compared to the best results of other compared methods for the criteria of accuracy, precision, recall, and F1. The implementation code of the proposed method is available at the following GitHub address: https://github.com/HadiAlhares/Covid19