Cover
Vol. 17 No. 2 (2021)

Published: December 31, 2021

Pages: 176-182

Review Article

Detection of Covid-19 Based on Chest Medical Imaging and Artificial Intelligent Techniques: A Review

Nawres Aref 1 Hussain Kareem
1Electrical Engineering Department, University of Technology, Baghdad, Iraq
History
  • Received: May 8, 2021
  • Revised: September 16, 2021
  • Accepted: September 21, 2021
  • Available Online: September 26, 2021
DOI

https://doi.org/10.37917/ijeee.17.2.19

Abstract

Novel Coronavirus (Covid-2019), which first appeared in December 2019 in the Chinese city of Wuhan. It is spreading rapidly in most parts of the world and becoming a global epidemic. It is devastating, affecting public health, daily life, and the global economy. According to the statistics of the World Health Organization on August 11, the number of cases of coronavirus (Covid-2019) reached nearly 17 million, and the number of infections globally distributed among most European countries and most countries of the Asian continent, and the number of deaths from the Corona virus reached 700 thousand people around the world. . It is necessary to detect positive cases as soon as possible in order to prevent the spread of this epidemic and quickly treat infected patients. In this paper, the current literature on the methods used to detect Covid is presented. In these studies, the research that used different techniques of artificial intelligence to detect COVID-19 was reviewed as the convolutionary neural network (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) were proposed for the identification of patients infected with coronavirus pneumonia using chest X-ray radiographs By using 5-fold cross validation, three separate binary classifications of four grades (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) were introduced. It has been shown that the pre-trained ResNet50 model offers the highest classification performance (96.1 percent accuracy for Dataset-1, 99.5 percent accuracy for Dataset-2 and 99.7 percent accuracy for Dataset-2) based on the performance results obtained.

Keywords

References

  1. T. Ji, Zhenwei Liu, G. Wang, Xu. Guo, S. Akbar khan, Changchun Lai, H. Chen, S. Huang, S. Xia, Bo Chen, Hongyun Jia, Y. Chen and Qiang Zhou, "Detection of COVID-19: A review of the current literature and future perspectives", Biosensors and Bioelectronics, Vol. 166, 2020.
  2. K. Roosa, Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., Yan, P., and Chowell, G., "Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020”, Infectious Disease Modelling, Vol. 5, pp. 256-263, 2020.
  3. Yan, L., Zhang, H. T., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Li, S., Zhang, M., Xiao, Y., Cao, H., Chen, Y., Ren, T., Jin, J., Wang, F., Xiao, Y., Huang, S., Tan, X., Huang, N., Jiao, B., Zhang, Y., Luo, A., Cao, Z., Xu, H., and Yuan, Y., "Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan", medRxiv, 2020.
  4. S. B. Stoecklin, P. Rolland, Y. Silue, A. Mailles, C. Campese, A. Simondon, M. Mechain, L. Meurice, M. Nguyen, C. Bassi, , E. Yamani, , S. Behillil, S. Ismael, D., Malvy, D.Nguyen, F. Lescure, X., Georges, S., Lazarus, C., Tabaï, A., Stempfelet, M., Enouf, V., Coignard, B., Levy-Bruhl, D. and Team, I., "First cases of coronavirus disease (COVID-19) in France: surveillance, investigations and control measures", Eurosurveillance, Vol. 25, No. 6, 2020.
  5. Coronavirus. World Health Organization: (2020) https://www.who.int/health-topics/coronavirus.
  6. C. Huang, Y. Wang, 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., Xiao, Y., Gao, H., Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin Q., Wang, J., and Cao, B., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.”, The Lancet, Vol. 395, No. 10223, pp. 497-506, 2020.
  7. A. Adedoyin Hussain, O. Bouachir, F. Al-Turjman and M. Aloqaily, "AI Techniques for COVID-19", IEEE Access, 2020.
  8. TB. Murdoch and AS. Detsky, "The inevitable application of big data to health care", JAMA, Vol. 309, PP. 1351–2, 2013.
  9. Kolker E, Özdemir V, and Kolker E. (2016) "How Healthcare can refocus on its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses)", by Leveraging Lessons from Amazon, Uber, and Watson. OMICS 20, pp. 329–33.
  10. Dilsizian SE and Siegel EL., "Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment", Curr Cardiol Rep, Vol. 16, No. 441, 2014.
  11. Xu X, P. Chen, and J. Wang, "Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission", Sci China Life Sci, 2020, DOI: 10.1007/s11427-020- 1637-5.
  12. Administration UFaD. Guidance for industry: electronic source data in clinical investigations, 2020. [Online] Available: https://www. fda. gov/ downloads/drugs/ guidances/ ucm328691.
  13. RJ. Gillies, PE. Kinahan, Hrica and H. Radiomics, "Images are more than pictures, they are data”, Radiology, Vol. 278, No. 2, pp. 563–77, 2016.
  14. Li. CY, GY. Liang and Yao WZ., "Integrated analysis of long noncoding RNA competing interactions reveals the potential role in progression of human gastric Cancer", Int J Oncol Vol. 48, pp. 1965–76, 2016.
  15. Shin H, Kim KH and Song C., "Electro-diagnosis support system for localizing neural injury in an upper limb”, J Am Med Inform Assoc, 2016.
  16. G. Karakülah, O. Dicle and Koşaner O., "Computer based extraction of phenotypic features of human congenital anomalies from the digital literature with natural language processing techniques”, Stud Health Technology Inform, Vol. 205, pp. 570–4, 2014.
  17. Becky McCall., "COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread", Published by Elsevier Ltd, 2020. Doi: 10.1016, S2589-7500(20)30054-6.
  18. Commissioner of the Statement from FDA Commissioner Scott Gottlieb, M.D. on steps toward a new, tailored review framework for artificial intelligence-based medical devices. FDA. (2019) [Online] Available: https://news-events/press-announcements/statement-fda- commissioner-scott-gottlieb-mdsteps.
  19. Al-Turjman F. Intelligence and security in big 5G-oriented IoNT: An overview, Elsevier Future Generation Computer Systems, Vol. 102, pp. 357–368, 2020.
  20. J. Bedford, D. Enria, J. Giesecke, D.L. Heymann, C. Ihekweazu, V. Kobinger, Lane H. C, Memish V, Oh M.D and Schuchat A., "COVID-19: towards controlling of a pandemic", Lancet, Vol. 395, pp. 1015–1018, 2020.
  21. F. Chollet, "Xception: deep learning with depthwise separable convolutions", in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258, 2017.
  22. M. G, Lina J.-M, and G. Kaddoum, "Artificial neural network for in-bed posture classification using bed-sheet pressure sensors", IEEE Journal of biomedical and health informatics, no. 10, pp. 101–110, 2020.
  23. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A and 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", arXiv preprint arXiv:05037, 2020.
  24. A. Ranjan, V. Jampani, L. Balles, K. Kim, D. Sun, J. Wulff, and M. J Black, "Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12240–12249, 2019.
  25. A. Zihao Zhu, L. Yuan, K. Chaney, and K. Daniilidis, "Unsupervised event-based learning of optical flow, depth, and egomotion", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 989–997, 2019.
  26. W. Naude, "Artificial Intelligence against COVID-19": An Early Review, Vol. 23, No. 4, 2020 [Online] Available: https://towardsdatascience.com/artificial-intelligence-aga inst-covid-19-an-early-review-92a8360edaba.
  27. M. Smolaks, “Deep learning systems can speed up diagnosis of Covid-19 patients", 2020. Available: https://aibusiness.com/deep-learning-systems-can-identif y-covid-19-infections-on-x-rays.
  28. Sh. Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng and Bo Xu., "A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)", MedRxiv preprint, 2020. Doi: https://doi.org/10.1101/2020.02.14.20023028. Aref & Kareem
  29. A. Devereaux, MD. Christian and JR. Dichter, “Summary of suggestions from the task force for mass critical care Summit”, Chest, Vol. 133, 2018. Doi:10.1378/chest.08-0649.
  30. N.E. Huang, and F. Qiao, "A data driven time ‐ dependent transmission rate for tracking an epidemic: a case 305 study of 2019‐nCoV", Science Bulletin, 2019. [Online] Available: https://doi.org/10.1016/j.scib.02.005. 2019
  31. Y. Yang, "Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China", medRxiv, 2020. https://doi.org/10.1101/2020.02.10.20021675.
  32. Wu, J.T., K. Leung, and G.M. Leung, "Now casting and forecasting the potential domestic and international 311 spread of the 2019‐nCoV outbreak originating in Wuhan, China", a modelling study. Lancet, 2020. Doi: 312 10.1016/S0140‐6736 (20)30260‐9.
  33. A. Narin, C. Kaya, and Pamuk, Z., "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks", arXiv, 2020.
  34. C. Zheng, X. Deng, Fu, Q., Zhou, Q., Feng, J., et al., "Deep learning-based detection for COVID-19 from chest CT using weak label", medRxiv, 2020. DOI: https://doi.org/10.1101/.03.12.20027185.
  35. P.K. Sethy, S.K. Behera, "Detection of Coronavirus Disease (COVID-19) Based on Deep Features", vol.1, reprints, 2020 (doi:10.20944/preprints202003.0300.v1)
  36. Y. Song, S. Zheng, L. Li, X. Zhang, X. Zhang, Z. Huang, Y. Chong, "Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images", medRxiv, 2020.
  37. E.E.D. Hemdan, M.A. Shouman, M.E. Karar, "COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images", arXiv preprint, 2020.
  38. X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, et al., “Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia”, arXiv preprint, 2020.
  39. T. Ozturk, M. Talo, EA. Yildirim, UB. Baloglu, Yildirim O, Acharya R., "Automated detection of COVID-19 cases using deep neural networks with X-ray images", Comput. Biol. Med., Vol. 121, 2020. https://doi.org/10.1016/j.compbiomed. 103792.
  40. Apostolopoulos ID, Mpesiana TA., “COVID-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks”, Physical and Engineering Sciences in Medicine, Vol. 43, pp.635–40, 2020. https://doi.org/10. 1007/s13246- 020- 00865- 4, 2020.
  41. Ioannis D. Apostolopoulos1, Tzani Bessiana, "COVID-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks", arXiv:11617, 2003.
  42. S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, B. Xu, " A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)", medRxiv, 2020.
  43. I. S. Abed, "Lung Cancer Detection from X-ray images by combined Backpropagation Neural Network and PCA”, Engineering and Technology Journal, Vol. 37, Part A, No. 05, pp. 166-171, 2019.
  44. A..S. Yaseen, R. S. Zamel and J. H. Khlaief,(2021), “Wavelet-Based Denoising Of Images,” Engineering and Technology Journal, Vol. 37, Part B, No. 2, pp. 54-60, 2019.
  45. H. M. Ahmed, and M. A. Taha, “A Brief Survey on Modern Iris Feature Extraction Methods”, Engineering and Technology Journal, Vol. 39,Part A, No. 01, pp. 123-129, 2021.
  46. H. .N. Abdullah and N.H. Abdulghafoor, “Automatic objects detection and tracking using FPCP, Blob analysis and kalman filter”, Engineering and Technology Journal, Vol. 38, No. 02, pp. 246-254, 2020.
  47. H.A. Jeiad, “Indian Number Handwriting Features Extraction and Classification using Multi-Class SVM”, Engineering and Technology Journal, Vol. 36, No. 1, pp. 33-40, 2018.