@Article{cmc.2021.018040,
AUTHOR = {Yu-Dong Zhang, Muhammad Attique Khan, Ziquan Zhu, Shui-Hua Wang},
TITLE = {Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {69},
YEAR = {2021},
NUMBER = {3},
PAGES = {3145--3162},
URL = {http://www.techscience.com/cmc/v69n3/44144},
ISSN = {1546-2226},
ABSTRACT = {(<i>Aim</i>) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (<i>Method</i>) This study aims to propose a novel deep learning 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 classifier. Besides, multiple-way data augmentation is chosen to overcome overfitting. 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. (<i>Results</i>) 10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06% ± 1.54%, a specificity 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. (<i>Conclusion</i>) 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.},
DOI = {10.32604/cmc.2021.018040}
}