Abstract
The pandemic COVID19, caused by a member of the coronavirus family (SARS-COV-2), occurred in Wuhan in 2019 and can cause the severe acute respiratory syndrome, fatal complications, and death. One of the main problems of the twenty-first century is coronavirus illness (COVID-19), which SARS-CoV-2 brings on. This virus, which has killed 6,654,361 people globally and infected 649,605,347 people, is currently causing widespread global conflict. Due to the high infection incidence in China and other nations within a short time, the WHO (World Health Organization) declared it a pandemic on February 11, 2020. Numerous individuals perished as a result of the COVID-19 pandemic epidemic. This virus has already infected millions of individuals, and new infections are occurring every day. Researchers are attempting to employ medical imaging like CT and X-ray images to identify COVID-19 With the use of modern AI technologies both the cost and time required to run a traditional RT-PCR test for detection is prohibitive. In order to combat the impacts of the new coronavirus illness, this research presents a thorough assessment of using artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) approaches in identifying (COVID-19). The primary objectives of this paper lie in identifying the broad and important outlines of the process of systematically researching the mechanisms of detection and classification of the Coronavirus through a review of a group of researchers; in addition, to compile the most used methods for independently identifying COVID-19 from medical images, and knowing the data sources of cross-sectional images. The researchers relied on Lung and x-rays in identifying the process, Furthermore, to discussing the accuracy of the results of the research through a review of the machine learning and general learning techniques used by the researchers are discussed. So, a novice researcher can evaluate prior works and come up with a better solution.
Keywords
COVID-19, Deep Learning, Medical Image, Artificial intelligence, coronavirus, computed tomography, CT images, Machine learning, X-ray.
References
Ahmad Alimadadi, Sachin Aryal, Ishan Manandhar, Patricia B. Munroe, Bina Joe, and Xi Cheng1, “Artificial intelligence and machine learning to fight COVID-19”, the American Physiological Society, (185.135.196.008), 2020.
Ahmad M. Sarhan, 2020, “Detection of COVID-19 Cases In Chest X-ray Images Using Wavelets And Support Vector Machines”, Department of Computer Engineering, Amman Arab University, Jordan
Al-Karawi, D., Al-Zaidi, S., Polus, N., & Jassim, S. (2020). Machine learning analysis of chest CT scan images as a complementary digital test of coronavirus (COVID-19) patients. MedRxiv.
Albahri, O. S., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Abdulkareem, K. H., Al-qaysi, Z. T., & et al. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13(10), 1381–1396. https://doi.org/ 10.1016/j.jiph.2020.06.028.
Alsharif, M. H., Alsharif, Y. H., Chaudhry, S. A., Albreem, M. A., Jahid, A., & Hwang, E. (2020). Artificial intelligence technology for diagnosing COVID-19 cases: A review of substantial issues. European Review for Medical and Pharmacological Sciences, 24(17),9226–9233
Ashraf Abdulmunim Abdulmajeed, Nada Nimat Saleem “Intelligent Tool for Detecting Covid-19 using Convolutional Neural Network based on both CT and X-Ray Lung Images” AIP Conf. Proc. 2404, 030002-1–030002-13,2021.
Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal, 18, 784–790.
Diego Hernandez, Rodrigo Pereira, Petia Georgevia, “COVID-19 detection through X-Ray chest images”, IEEE, 21:30:09 UTC, 2022.
Elzeki, Omar M., Shams, Mahmoud, Sarhan, Shahenda, Elfattah, Mohamed Abd, & Has-sanien, Aboul Ella (2021). COVID-19: A new deep learning computer-aided model for classification. PeerJournal of Computer Science, 7, e358 33 pages.
F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19,” IEEE Rev. Biomed. Eng., pp. 1–13, Apr. 2020.
Fang, Y. et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology https ://doi.org/10.1148/radio l.20202 00432(2020).
Farid, G. I. Selim, and H. A. A. Khater, “A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19),” 2020.
Ghulam Gilanie, Usama Ijaz Bajwa, Mustansar Mahmood Waraich, Mutyyba Asghar, Rehana Kousar, Adnan Kashif, Rabab Shereen Aslam, Muhammad Mohsin Qasim, Hamza Rafique, “Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks”, Elsevier, https://doi.org/10.1016/j.bspc.2021.102490, 2021.
Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E. Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis. arXiv2003.05037. 2020.
J. Ying- Hui, et al, “b”, (Military Medical Research 7.1, 2020).
Jamshidi, Mohammad, et al. “Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment.” IEEE Access 8 (2020): 109581-109595.
Jin et al., “Development and Evaluation of an AI System for COVID-19 Diagnosis,” 2020.
Khan M, Mehran MT, Haq ZU, Ullah Z, Naqvi SR, Ihsan M, Abbass H. Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert systems with applications. 2021 Dec 15;185:115695.
L. Wang, A. Wong, COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images, arXiv preprint arXiv:2003.09871, 2020.
Lu S, Lu Z, Zhang YD. Pathological brain detection based on AlexNet and transfer learning. J Comp Sci 2019; 30:41–7.
Mbunge, E. (2020). Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(6), 1631–1636.
McCall, “COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread,” Lancet Digit. Heal., vol. 2, no. 4, pp. e166–e167, Apr. 2020.
Medhi, K.; Jamil, M.; Hussain, M.I. Automatic detection of COVID-19 infection from chest X-ray using deep learning. Medrxiv 2020.
Mehmet Akif Cifci, “Deep Learning Model for Diagnosis of Corona Virus Disease from CT Images,” Int. J. Sci. Eng. Res., vol. 11, no. 4, pp. 273–278, 2020.
Metsky HC, Freije CA, Kosoko-Thoroddsen T-SF, Sabeti PC, Myhrvold C. CRISPR-based COVID-19 surveillance using a genomicallycomprehensive machine learning approach. bioRxiv, 2020. doi:10.1101/ 2020.02.26.967026.
New machine learning method for imagebased diagnosis of COVID-19 Mohamed Abd Elaziz1,2, Khalid M. Hosny, Ahmad Salah3, Mohamed M. Darwish4, Songfeng Lu2, Ahmed T. Sahlol5
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine 121:103792.
P. G. B. Moutounet-Cartan, “Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays,” May 2020.
Peng, S.; Pan, L.; Guo, Y.; Gong, B.; Huang, X.; Liu, S.; Huang, J.; Pu, H.; Zeng, J. Quantitative CT imaging features for COVID-19 evaluation: The ability to differentiate COVID-19 from non-COVID-19 (highly suspected) pneumonia patients during the epidemic period. PLoS ONE 2022, 17, e0256194. [CrossRef] [PubMed].
R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 337–339, Jul. 2020.
Riva, L., Yuan, S., Yin, X., Martin-Sancho, L., Matsunaga, N., Pache, L., … Chanda, S. K. (2020). Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing. Nature, 586(7827), 113–119
S. R. Amine Amyar, Romain Modzelewski, “Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation,” 2020.
Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature 577: 706 –710, 2020. doi:10.1038/s41586-019- 1923-7.
Seung Hoon Yoo, Hui Geng, Tin Lok Chiu, Siu Ki Yu, Dae Chul Cho, Jin Heo, Min Sung Choi, Il Hyun Choi, Cong Cung Van, Nguen Viet Nhung, Byung Jun Min and Ho Lee,2020,” Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging”, Frontiers in Medicine, Volume 7, Article 427.
Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., Jiang H, He Y, Gao Y, Sui H, Shen, D. Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification (2020). arXiv preprint arXiv:2003.09860.
Sipior, Janice C. (2020). Considerations for development and use of AI in response to COVID-19. International Journal of Information Management, 55, 102170
Srikanth Tammina, 2020, “CovidSORT: Detection of novel COVID – 19 in chest X-ray images by leveraging deep transfer learning models “, ResearchGate.
Swapnarekha, H., Behera, H. S., Nayak, J., & Naik, B. (2020). Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos, Solitons and Fractals, 138, 109947
Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR. 2020. Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection. IEEE Access 8:91916–91923.
Waleed Salehi, A., Baglat, P., & Gupta, G. (2020). Review on Machine and Deep Learning Models for the Detection and Prediction of Coronavirus. Materials Today: Proceedings, 33, 3896–3901
Wang et al., “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19),” 2020.
Wang Y, Hu M, Li Q, Zhang X-P, Zhai G, Yao N. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv2002.05534. 2020.
X. Wu et al., “Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study,” Eur. J. Radiol., vol. 128, p. 109041, Jul. 2020.
X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, et al., Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia, 2020 arXiv preprint arXiv:200209334.Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov DS, Polykovskiy D, Shayakhmetov R, Filimonov A, Orekhov P. Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches. Insilico Med Hong Kong Ltd A 307: E1, 2020.