This paper proposes a Convolutional Neural Networks (CNN) based model for the diagnosis of COVID-19 and non-COVID-19 viral pneumonia diseases. These diseases affect and damage the human lungs. Early diagnosis of patients infected by the virus can help save the patient's life and prevent the further spread of the virus. The CNN model is used to help in the early diagnosis of the virus using chest X-ray images, as it is one of the fastest and most cost-effective ways of diagnosing the disease. We proposed two convolutional neural networks (CNN) models, which were trained using two different datasets. The first model was trained for binary classification with one of the datasets that only included pneumonia cases and normal chest X-ray images. The second model made use of the knowledge learned by the first model using transfer learning and trained for 3 class classifications on COVID-19, pneumonia, and normal cases based on the second dataset that included chest X-ray (CXR) images. The effect of transfer learning on model constriction has been demonstrated. The model gave promising results in terms of accuracy, recall, precision, and F1_score with values of 98.3%, 97.9%, 98.3%, and 98.0%, respectively, on the test data. The proposed model can diagnose the presence of COVID-19 in CXR images; hence, it will help radiologists make diagnoses easily and more accurately.
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