ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.018040 Article Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis Yu-Dong Zhang 1 , Muhammad Attique Khan 2 , Ziquan Zhu 3 and Shui-Hua Wang 4, * 1 School of Informatics, University of Leicester, Leicester, LE1 7RH, UK 2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan 3 Science in Civil Engineering, University of Florida, Gainesville, Florida, FL 32608, Gainesville, USA 4 School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK * Corresponding Author: Shui-Hua Wang. Email: shuihuawang@ieee.org Received: 22 February 2021; Accepted: 07 April 2021 Abstract: (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confrmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method ) This study aims to propose a novel deep learn- ing method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifer. Besides, multiple-way data augmentation is chosen to overcome overftting. The multiple-way data augmentation is based on Gaussian noise, salt-and-pepper noise, speckle noise, horizontal and vertical shear, rotation, Gamma correction, random translation and scaling. (Results) 10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06% ± 1.54%, a specifcity of 92.56% ± 1.06%, a precision of 92.53% ± 1.03%, and an accuracy of 92.31% ± 1.08%. Its F1 score, MCC, and FMI arrive at 92.29% ±1.10%, 84.64% ± 2.15%, and 92.29% ± 1.10%, respectively. The AUC of our model is 0.9576. (Conclusion) We demonstrate “image plane over unit circle” can get better results than “image plane inside a unit circle.” Besides, this proposed PZM-DSSAE model is better than eight state-of-the-art approaches. Keywords: Pseudo Zernike moment; stacked sparse autoencoder; deep learning; COVID-19; multiple-way data augmentation; medical image analysis 1 Introduction COVID-19 has caused more than 107.45 m confrmed cases and 2.35 m deaths till 11/Feb/2021 in about 192 countries/regions and 26 cruise/naval ships [1]. Fig. 1 shows the top 10 countries of cumulative confrmed cases and deaths, respectively. The main symptoms of COVID-19 are low fever, a new and ongoing cough, a loss or change to taste and smell [2]. In the UK, This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.