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

Muhammad Owais, Haseeb Sultan, Na Rae Baek, Young Won Lee, Muhammad Usman, Dat Tien Nguyen, Ganbayar Batchuluun and Kang Ryoung Park ()
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Muhammad Owais: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Haseeb Sultan: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Na Rae Baek: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Young Won Lee: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Muhammad Usman: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Dat Tien Nguyen: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Ganbayar Batchuluun: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Kang Ryoung Park: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea

Mathematics, 2022, vol. 10, issue 21, 1-15

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.

Keywords: DSS-Net; artificial intelligence; COVID-19 diagnosis; content-based retrieval; lung disease (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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