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The outbreak of COVID-19 disease caused more than 100,000 deaths so far inthe USA alone. It is necessary to conduct an initial screening of patients withthe symptoms of COVID-19 disease to control the spread of the disease. However,it is becoming laborious to conduct the tests with the available testing kitsdue to the growing number of patients. Some studies proposed CT scan or chestX-ray images as an alternative solution. Therefore, it is essential to useevery available resource, instead of either a CT scan or chest X-ray to conducta large number of tests simultaneously. As a result, this study aims to developa deep learning-based model that can detect COVID-19 patients with betteraccuracy both on CT scan and chest X-ray image dataset. In this work, eightdifferent deep learning approaches such as VGG16, InceptionResNetV2, ResNet50,DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2 have been testedon two dataset-one dataset includes 400 CT scan images, and another datasetincludes 400 chest X-ray images studied. Besides, Local InterpretableModel-agnostic Explanations (LIME) is used to explain the model'sinterpretability. Using LIME, test results demonstrate that it is conceivableto interpret top features that should have worked to build a trust AI frameworkto distinguish between patients with COVID-19 symptoms with other patients.

machine learning and knowledge extraction

Study of Different Deep Learning Approach with Explainable AI for  Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray  Image Dataset