PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

 

e-ISSN 2231-8526
ISSN 0128-7680

Home / Regular Issue / JST Vol. 29 (3) Jul. 2021 / JST-2352-2020

 

Detection of COVID-19 from Chest X-ray and CT Scan Images using Improved Stacked Sparse Autoencoder

Syahril Ramadhan Saufi, Muhd Danial Abu Hasan, Zair Asrar Ahmad, Mohd Salman Leong and Lim Meng Hee

Pertanika Journal of Science & Technology, Volume 29, Issue 3, July 2021

DOI: https://doi.org/10.47836/pjst.29.3.14

Keywords: COVID-19, CT scan, deep learning, image classification, X-ray

Published on: 31 July 2021

The novel Coronavirus 2019 (COVID-19) has spread rapidly and has become a pandemic around the world. So far, about 44 million cases have been registered, causing more than one million deaths worldwide. COVID-19 has had a devastating impact on every nation, particularly the economic sector. To identify the infected human being and prevent the virus from spreading further, easy, and precise screening is required. COVID-19 can be potentially detected by using Chest X-ray and computed tomography (CT) images, as these images contain essential information of lung infection. This radiology image is usually examined by the expert to detect the presence of COVID-19 symptom. In this study, the improved stacked sparse autoencoder is used to examine the radiology images. According to the result, the proposed deep learning model was able to achieve a classification accuracy of 96.6% and 83.0% for chest X-ray and chest CT-scan images, respectively.

  • Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640. https://doi.org/10.1007/s13246-020-00865-4

  • Bustin, S. A., & Nolan, T. (2020). RT-qPCR testing of SARS-CoV-2: A primer. IInternatIonal Journal of Molecular Sciences, 21(8), Article 3004. https://doi.org/10.3390/ijms21083004

  • Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020). GitHub - ieee8023/covid-chestxray-dataset: We are building an open database of COVID-19 cases with chest X-ray or CT images. Retrieved March 22, 2021, from https://github.com/ieee8023/COVID-chestxray-dataset

  • He, X., Yang, X., Zhang, S., Zhao, J., Zhnag, Y., Xing, E., & Xie, P. (2020). Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. Retrieved March 22, 2021, from https://github.com/UCSD-AI4H/COVID-CT

  • Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. ArXiv, 1-14.

  • Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5

  • Luo, L., Xiong, Y., Liu, Y., & Sun, X. (2019). Adaptive gradient methods with dynamic bound of learning rate. ArXiv:1902.09843, 2018, 1-19.

  • Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, Article 103792. https://doi.org/10.1016/j.compbiomed.2020.103792

  • Purohit, K., Kesarwani, A., Kisku, D. R., & Dalui, M. (2020). COVID-19 detection on chest X-Ray and CT scan images using multi-image augmented deep learning model. BioRxiv, 15-22. https://doi.org/10.1101/2020.07.15.205567

  • Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In IEEE international conference on neural networks (pp. 586-591). IEEE Conference Publication. https://doi.org/10.1109/ICNN.1993.298623

  • Salehi, S., Abedi, A., Balakrishnan, S., & Gholamrezanezhad, A. (2020). Coronavirus disease 2019 (COVID-19): A systematic review of imaging findings in 919 patients. American Journal of Roentgenology, 215(1), 87-93. https://doi.org/10.2214/AJR.20.23034

  • Saufi, S. R., Ahmad, Z. A., Leong, M. S., & Lim, M. H. (2019). Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review. IEEE Access, 7(1), 122644-122662. https://doi.org/10.1109/ACCESS.2019.2938227

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929-1958. https://doi.org/10.1214/12-AOS1000

  • Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. https://doi.org/10.1016/j.aci.2018.08.003

  • Verstraete, D., Ferrada, A., Droguett, E. L., Meruane, V., & Modarres, M. (2017). Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Hindawi Shock and Vibration, 2017, 1-29. https://doi.org/10.1155/2017/5067651

  • Wahab, M. N. A., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PLoS ONE, 10(5), 1-36. https://doi.org/10.1371/journal.pone.0122827

  • Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2020). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv, 1-23. https://doi.org/https://doi.org/10.1101/2020.02.14.20023028

  • Wang, Y., Liu, M., Bao, Z., & Zhang, S. (2018). Stacked sparse autoencoder with PCA and SVM for data-based line trip fault diagnosis in power systems. Neural Computing and Applications, 5, 1-13. https://doi.org/10.1007/s00521-018-3490-5

  • Ying, S., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., Shen, J., Zha, Y., & Yang, Y. (2020). Deep learning enables accurate diagnosis of novel Coronavirus (COVID-19) with CT images. MedRxiv, 1-10. https://doi.org/10.1101/2020.02.23.20026930

  • Yang, W., Sirajuddin, A., Zhang, X., Liu, G., Teng, Z., Zhao, S., & Lu, M. (2020). The role of imaging in 2019 novel coronavirus pneumonia (COVID-19). European Radiology, 30, 4874-4882. https://doi.org/10.1007/s00330-020-06827-4

  • Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., & Xie, P. (2020). COVID-CT-dataset: A CT scan dataset about COVID-19. ArXiv Preprint ArXiv:2003.13865, 1-14.

  • Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020). Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv, 1-13. https://doi.org/10.1101/2020.03.12.20027185

  • Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020). Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology, 296(2), E15-E25. https://doi.org/10.1148/radiol.2020200490

ISSN 0128-7680

e-ISSN 2231-8526

Article ID

JST-2352-2020

Download Full Article PDF

Share this article

Recent Articles