COVID-19 Diagnosis Using Deep Learning Algorithms Based on Chest CT Scan: A Survey
DOI:
https://doi.org/10.29304/jqcm.2023.15.3.1274Keywords:
Coronavirus Diagnosis, Chest CT scan, Deep Learning, COVID-19Abstract
The coronavirus (SARS-CoV-2), which causes severe acute respiratory syndrome (COVID-19), is the most current virus with the potential to kill a substantial number of people around the world. Current procedures are restrictive, time-consuming, inefficient, and obsolete. This is due to their exclusive reliance on the technical expertise of a radiologist or medical consultant. Deep learning and technological advancement have also enabled medical researchers and scientists to investigate various neural networks and algorithms to develop apps, tools, and gadgets that can assist medical radiologists. This paper aims to give an overview of deep learning techniques that have been used in chest radiography of COVID-19 and pneumonia cases, as well as the most important datasets that help researchers test algorithms.
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References
[1] W. C. Serena Low et al., “An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19,” Comput. Math. Methods Med., vol. 2021, 2021, doi: 10.1155/2021/5528144.
[2] Y. Chen et al., “A Survey on Artificial Intelligence in Chest Imaging of COVID-19,” BIO Integr., vol. 1, no. 3, pp. 137–146, 2020, doi: 10.15212/bioi-2020-0015.
[3] F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19,” IEEE Rev. Biomed. Eng., vol. 14, pp. 4–15, 2021, doi: 10.1109/RBME.2020.2987975.
[4] S. Wang et al., “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19),” Eur. Radiol., vol. 31, no. 8, pp. 6096–6104, 2021.
[5] A. Kovács, P. Palásti, D. Veréb, B. Bozsik, A. Palkó, and Z. T. Kincses, “The sensitivity and specificity of chest CT in the diagnosis of COVID-19,” Eur. Radiol., vol. 31, no. 5, pp. 2819–2824, 2021.
[6] V. Perumal, V. Narayanan, and S. J. S. Rajasekar, “Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features,” Appl. Intell., vol. 51, no. 1, pp. 341–358, 2021, doi: 10.1007/s10489-020-01831-z.
[7] P. Afshar et al., “COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning,” Sci. Data, vol. 8, no. 1, pp. 1–8, 2021, doi: 10.1038/s41597-021-00900-3.
[8] E. Çallı, E. Sogancioglu, B. van Ginneken, K. G. van Leeuwen, and K. Murphy, “Deep learning for chest X-ray analysis: A survey,” Med. Image Anal., vol. 72, 2021, doi: 10.1016/j.media.2021.102125.
[9] N. Gianchandani, A. Jaiswal, D. Singh, V. Kumar, and M. Kaur, “Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2020, doi: 10.1007/s12652-020-02669-6.
[10] S. Serte and H. Demirel, “Deep learning for diagnosis of COVID-19 using 3D CT scans,” Comput. Biol. Med., vol. 132, no. February, p. 104306, 2021, doi: 10.1016/j.compbiomed.2021.104306.
[11] O. Gozes, M. Frid-Adar, N. Sagie, H. Zhang, W. Ji, and H. Greenspan, “Coronavirus detection and analysis on chest ct with deep learning,” arXiv Prepr. arXiv2004.02640, 2020.
[12] Y. Song et al., “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 18, no. 6, pp. 2775–2780, 2021.
[13] T. Anwar and S. Zakir, “Deep learning based diagnosis of COVID-19 using chest CT-scan images,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1–5.
[14] V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, and N. Mehendale, “Diagnosis of COVID-19 using CT scan images and deep learning techniques,” Emerg. Radiol., vol. 28, no. 3, pp. 497–505, 2021.
[15] M. Roberts et al., “Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans,” Nat. Mach. Intell., vol. 3, no. 3, pp. 199–217, 2021.
[16] F. Liu et al., “The application of artificial intelligence to chest medical image analysis,” Intell. Med., vol. 1, no. 3, pp. 104–117, 2021, doi: 10.1016/j.imed.2021.06.004.
[17] J. Liu, H. Yu, and S. Zhang, “The indispensable role of chest CT in the detection of coronavirus disease 2019 (COVID-19),” Eur. J. Nucl. Med. Mol. Imaging, vol. 47, no. 7, pp. 1638–1639, 2020.
[18] C. A. Raptis et al., “Chest CT and coronavirus disease (COVID-19): a critical review of the literature to date,” AJR Am J Roentgenol, vol. 215, no. 4, pp. 839–842, 2020.
[19] Y. Li and L. Xia, “Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management,” Ajr Am J Roentgenol, vol. 214, no. 6, pp. 1280–1286, 2020.
[20] J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, “COVID-19 open source data sets: a comprehensive survey,” spring Sci., vol. 3, 2020.
[21] X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, and P. Xie, “COVID-CT-Dataset: A CT Scan Dataset about COVID-19,” arXiv Prepr. arXiv1105.3668, vol. 3, pp. 1–14, 2020, [Online]. Available: http://arxiv.org/abs/2003.13865.
[22] E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” medRxiv, p. 2020.04.24.20078584, 2020, [Online]. Available: http://medrxiv.org/content/early/2020/05/14/2020.04.24.20078584.abstract.
[23] S. Shakouri et al., “COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis,” BMC Res. Notes, vol. 14, no. 1, pp. 1–3, 2021, doi: 10.1186/s13104-021-05592-x.
[24] “COVID-19 CT segmentation dataset,” http://medicalsegmentation.com/covid19/, [Online]. Available: http://medicalsegmentation.com/covid19/.
[25] T. Sakinis et al., “Interactive segmentation of medical images through fully convolutional neural networks,” arXiv Prepr. arXiv1105.3668, no. v, pp. 1–10, 2019, [Online]. Available: http://arxiv.org/abs/1903.08205.
[26] Https://zenodo.org/record/3757476#.YrTbmXZBzIV, “COVID-19 CT Lung and Infection Segmentation Dataset,” [Online]. Available: https://zenodo.org/record/3757476#.YrTbmXZBzIV.
[27] T. Technologies, “Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies Department of Health Care of Moscow www.mosmed.ai,” pp. 1–4, 2020.
[28] Y. You, J. Li, S. Reddi, J. Hseu, S. Kumar, and S. Bhojanapalli, “A fully automated Deep Learning-based network for detecting COVID-19 from a new and large lung ct scan dataset,” pp. 1–38, 2020.
[29] X. Wu, C. Chen, M. Zhong, J. Wang, and J. Shi, “Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information ,” no. January, 2020.
[30] P. gifani, A. Shalbaf, and M. Vafaeezadeh, “Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans,” Int. J. Comput. Assist. Radiol. Surg., vol. 16, no. 1, pp. 115–123, 2021, doi: 10.1007/s11548-020-02286-w.
[31] A. K. Mishra, S. K. Das, P. Roy, and S. Bandyopadhyay, “Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach,” J. Healthc. Eng., vol. 2020, 2020, doi: 10.1155/2020/8843664.
[32] T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo, and Y. Dong, “The ensemble deep learning model for novel COVID-19 on CT images,” Appl. Soft Comput., vol. 98, p. 106885, 2021, doi: 10.1016/j.asoc.2020.106885.
[33] M. Y. Kamil, “A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 844–850, 2021, doi: 10.11591/ijece.v11i1.pp844-850.
[2] Y. Chen et al., “A Survey on Artificial Intelligence in Chest Imaging of COVID-19,” BIO Integr., vol. 1, no. 3, pp. 137–146, 2020, doi: 10.15212/bioi-2020-0015.
[3] F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19,” IEEE Rev. Biomed. Eng., vol. 14, pp. 4–15, 2021, doi: 10.1109/RBME.2020.2987975.
[4] S. Wang et al., “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19),” Eur. Radiol., vol. 31, no. 8, pp. 6096–6104, 2021.
[5] A. Kovács, P. Palásti, D. Veréb, B. Bozsik, A. Palkó, and Z. T. Kincses, “The sensitivity and specificity of chest CT in the diagnosis of COVID-19,” Eur. Radiol., vol. 31, no. 5, pp. 2819–2824, 2021.
[6] V. Perumal, V. Narayanan, and S. J. S. Rajasekar, “Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features,” Appl. Intell., vol. 51, no. 1, pp. 341–358, 2021, doi: 10.1007/s10489-020-01831-z.
[7] P. Afshar et al., “COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning,” Sci. Data, vol. 8, no. 1, pp. 1–8, 2021, doi: 10.1038/s41597-021-00900-3.
[8] E. Çallı, E. Sogancioglu, B. van Ginneken, K. G. van Leeuwen, and K. Murphy, “Deep learning for chest X-ray analysis: A survey,” Med. Image Anal., vol. 72, 2021, doi: 10.1016/j.media.2021.102125.
[9] N. Gianchandani, A. Jaiswal, D. Singh, V. Kumar, and M. Kaur, “Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2020, doi: 10.1007/s12652-020-02669-6.
[10] S. Serte and H. Demirel, “Deep learning for diagnosis of COVID-19 using 3D CT scans,” Comput. Biol. Med., vol. 132, no. February, p. 104306, 2021, doi: 10.1016/j.compbiomed.2021.104306.
[11] O. Gozes, M. Frid-Adar, N. Sagie, H. Zhang, W. Ji, and H. Greenspan, “Coronavirus detection and analysis on chest ct with deep learning,” arXiv Prepr. arXiv2004.02640, 2020.
[12] Y. Song et al., “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 18, no. 6, pp. 2775–2780, 2021.
[13] T. Anwar and S. Zakir, “Deep learning based diagnosis of COVID-19 using chest CT-scan images,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1–5.
[14] V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, and N. Mehendale, “Diagnosis of COVID-19 using CT scan images and deep learning techniques,” Emerg. Radiol., vol. 28, no. 3, pp. 497–505, 2021.
[15] M. Roberts et al., “Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans,” Nat. Mach. Intell., vol. 3, no. 3, pp. 199–217, 2021.
[16] F. Liu et al., “The application of artificial intelligence to chest medical image analysis,” Intell. Med., vol. 1, no. 3, pp. 104–117, 2021, doi: 10.1016/j.imed.2021.06.004.
[17] J. Liu, H. Yu, and S. Zhang, “The indispensable role of chest CT in the detection of coronavirus disease 2019 (COVID-19),” Eur. J. Nucl. Med. Mol. Imaging, vol. 47, no. 7, pp. 1638–1639, 2020.
[18] C. A. Raptis et al., “Chest CT and coronavirus disease (COVID-19): a critical review of the literature to date,” AJR Am J Roentgenol, vol. 215, no. 4, pp. 839–842, 2020.
[19] Y. Li and L. Xia, “Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management,” Ajr Am J Roentgenol, vol. 214, no. 6, pp. 1280–1286, 2020.
[20] J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, “COVID-19 open source data sets: a comprehensive survey,” spring Sci., vol. 3, 2020.
[21] X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, and P. Xie, “COVID-CT-Dataset: A CT Scan Dataset about COVID-19,” arXiv Prepr. arXiv1105.3668, vol. 3, pp. 1–14, 2020, [Online]. Available: http://arxiv.org/abs/2003.13865.
[22] E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” medRxiv, p. 2020.04.24.20078584, 2020, [Online]. Available: http://medrxiv.org/content/early/2020/05/14/2020.04.24.20078584.abstract.
[23] S. Shakouri et al., “COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis,” BMC Res. Notes, vol. 14, no. 1, pp. 1–3, 2021, doi: 10.1186/s13104-021-05592-x.
[24] “COVID-19 CT segmentation dataset,” http://medicalsegmentation.com/covid19/, [Online]. Available: http://medicalsegmentation.com/covid19/.
[25] T. Sakinis et al., “Interactive segmentation of medical images through fully convolutional neural networks,” arXiv Prepr. arXiv1105.3668, no. v, pp. 1–10, 2019, [Online]. Available: http://arxiv.org/abs/1903.08205.
[26] Https://zenodo.org/record/3757476#.YrTbmXZBzIV, “COVID-19 CT Lung and Infection Segmentation Dataset,” [Online]. Available: https://zenodo.org/record/3757476#.YrTbmXZBzIV.
[27] T. Technologies, “Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies Department of Health Care of Moscow www.mosmed.ai,” pp. 1–4, 2020.
[28] Y. You, J. Li, S. Reddi, J. Hseu, S. Kumar, and S. Bhojanapalli, “A fully automated Deep Learning-based network for detecting COVID-19 from a new and large lung ct scan dataset,” pp. 1–38, 2020.
[29] X. Wu, C. Chen, M. Zhong, J. Wang, and J. Shi, “Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information ,” no. January, 2020.
[30] P. gifani, A. Shalbaf, and M. Vafaeezadeh, “Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans,” Int. J. Comput. Assist. Radiol. Surg., vol. 16, no. 1, pp. 115–123, 2021, doi: 10.1007/s11548-020-02286-w.
[31] A. K. Mishra, S. K. Das, P. Roy, and S. Bandyopadhyay, “Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach,” J. Healthc. Eng., vol. 2020, 2020, doi: 10.1155/2020/8843664.
[32] T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo, and Y. Dong, “The ensemble deep learning model for novel COVID-19 on CT images,” Appl. Soft Comput., vol. 98, p. 106885, 2021, doi: 10.1016/j.asoc.2020.106885.
[33] M. Y. Kamil, “A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 844–850, 2021, doi: 10.11591/ijece.v11i1.pp844-850.
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Published
2023-09-30
How to Cite
Hassona, M. A., & Hassan, T. M. (2023). COVID-19 Diagnosis Using Deep Learning Algorithms Based on Chest CT Scan: A Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(3), Comp Page 183–194. https://doi.org/10.29304/jqcm.2023.15.3.1274
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Computer Articles
