Application of Explainable Convolutional Neural Networks on the Differential Diagnosis of Covid_19 and Pneumonia using Chest Radiograph

Abstract

Abstract: The Covid_19 disease is one of the deadliest inflammatories and chronic and acute diseases of the human respiratory system, which is the result of the inhibition of a virus called corona in the respiratory organs since the spread of this virus is rapid and has affected many people in the world. a specialist needs to be carefully evaluated to diagnose the disease based on X-ray images because the number of patients with Covid_19 exceeds the capacity of hospitals and taking care of a large number of people is tedious work that can reduce the accuracy of the doctor in diagnosing the disease. In addition, in such cases, the absence of a specialist doctor can lead to misdiagnosis and incorrect prescribing. In this article, we intend to provide an approach to accelerate the diagnosis process and reduce the workload of specialists automatically, which in addition to helping physicians in hospitals that do not have a specialist physician, also allows patients to be diagnosed and treated. we use pre-trained UNet to extract the lung balloons, which eliminates the extra noise and parts in the X-ray image and then we give the generated images to a convolutional neural network model designed to diagnose and classify Covid_19 disease from Pneumonia, and finally, we use Grad-CAM and Vanilla Gradient and Smooth Grad techniques to validate the designed model. according to the results, our proposed approach using evaluation metrics was able to achieve the highest degree of accuracy in distinguishing Covid_19 disease from Pneumonia.