The purpose of this study is to devise a Computer Aided Diagnosis (CAD) system that is able to detect COVID-19 abnormalities from chest radio-graphs with increased efficiency and accuracy. We investigate a novel deep learning based ensemble model to classify the category of pneumonia from chest X-ray images. We use a labeled image dataset provided by Society for Imaging Informatics in Medicine for a kaggle competition that contains chest radio-graphs. And the task of our proposed CAD is to categorize between negative for pneumonia or typical, indeterminate, atypical for COVID-19. The training set (with labels publicly available) of this dataset contains 6334 images belonging to 4 classes. Furthermore, we experiment on the efficacy of our proposed ensemble method. Accordingly, we perform a ablation study to confirm that our proposed pipeline drives the classification accuracy higher and also compare our ensemble technique with the existing ones quantitatively and qualitatively.
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