Deep 3D Volumetric Model Genesis for Efficient Screening of Lung Infection Using Chest CT Scans

Abstract

In the present outbreak of COVID-19, radiographic imaging modalities such as computed tomography (CT) scanners are commonly used for visual assessment of COVID-19 infection. However, personal assessment of CT images is a time-taking process and demands expert radiologists. Recent advancement in artificial intelligence field has achieved remarkable performance of computer-aided diagnosis (CAD) methods. Therefore, various deep learning-driven CAD solutions have been proposed for the automatic diagnosis of COVID-19 infection. However, most of them consider limited number of data samples to develop and validate their methods. In addition, various existing methods employ image-based models considering only spatial information in making a diagnostic decision in case of 3D volumetric data. To address these limitations, we propose a dilated shuffle sequential network (DSS-Net) that considers both spatial and 3D structural features in case of volumetric CT data and makes an effective diagnostic decision. To calculate the performance of the proposed DSS-Net, we combined three publicly accessible datasets that include large number of positive and negative data samples. Finally, our DSS-Net exhibits the average performance of 96.58%, 96.53%, 97.07%, 96.01%, and 98.54% in terms of accuracy, F1-score, average precision, average recall, and area under the curve, respectively, and outperforms various state-of-the-art methods.