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A Review on Effectiveness of AI and ML Techniques for Classification of COVID-19 Medical Images

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Recent Advances in Artificial Intelligence and Data Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1386))

Abstract

During the global urgency, the scientific world is searching for new innovation and technology to tackle the pandemic. It is the necessity to develop a tool that will monitor and reduce human intervention in treating COVID-19. Major techniques/tools are available to control the current situation by diagnosing the patients with the help of machine learning (ML) and artificial intelligence (AI). These techniques have helped researchers in identifying/controlling the epidemic in past decades. The use of ML and AI has helped medical fraternity to significantly improve screening, testing, predicting and treating and helped in developing vaccines for COVID-19 patients. This article presents a view of how techniques like AI and ML are used for detecting, classifying and clustering of novel coronavirus disease 2019 by using the various types of medical images.

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References

  1. B.M. Henrique, V.A. Sobreiro, H. Kimura, Literature review: machine learning techniques applied to financial market prediction. Expert Syst. Appl. 124, 226–251 (2019)

    Article  Google Scholar 

  2. J. Singh, G. Singh, R. Singh, Optimization of sentiment analysis using machine learning classifiers. HCIS 7(1), 32 (2017)

    Google Scholar 

  3. M. Motwani, D. Dey, D.S. Berman, G. Germano, S. Achenbach, M.H. Al-Mallah, D. Andreini, M.J. Budoff, F. Cademartiri, T.Q. Callister et al., Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur. Heart J. 38(7), 500–507 (2017)

    Google Scholar 

  4. J.A. Sidey-Gibbons, C.J. Sidey-Gibbons, Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19(1), 64 (2019)

    Article  Google Scholar 

  5. T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, U.R. Acharya, Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput. Biol. Med. 103792 (2020)

    Google Scholar 

  6. V.K.R. Chimmula, L. Zhang, Time series forecasting of covid-19 transmission in canada using lstm networks. Chaos Solit. Fract. 109864 (2020)

    Google Scholar 

  7. M. Claesen, B. De Moor, Hyperparameter search in machine learning. arXiv:1502.02127 (2015)

  8. P. Song, T. Karako, Covid-19: real-time dissemination of scientific information to fight a public health emergency of international concern. Biosc. Trends (2020)

    Google Scholar 

  9. D.A. Leon, V.M. Shkolnikov, L. Smeeth, P. Magnus, M. Pechholdová, C.I. Jarvis, Covid-19: a need for real-time monitoring of weekly excess deaths. Lancet 395(10234), e81 (2020)

    Google Scholar 

  10. K. Liu, Y.-Y. Fang, Y. Deng, W. Liu, M.-F. Wang, J.-P. Ma, W. Xiao, Y.-N. Wang, M.-H. Zhong, C.-H. Li et al., Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei province. Chin. Med. J. (2020)

    Google Scholar 

  11. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497–506 (2020)

    Article  Google Scholar 

  12. J. Cai, J. Xu, D. Lin, L. Xu, Z. Qu, Y. Zhang, H. Zhang, R. Jia, X. Wang, Y. Ge et al., A case series of children with 2019 novel coronavirus infection: clinical and epidemiological features. Clin. Infect. Diseases (2020)

    Google Scholar 

  13. K. Kam, C. Yung, L. Cui et al., A well infant with coronavirus disease 2019 (covid-19) with high viral load. Clin Infect Dis. 10

    Google Scholar 

  14. Y. Bai, L. Yao, T. Wei, F. Tian, D.-Y. Jin, L. Chen, M. Wang, Presumed asymptomatic carrier transmission of covid-19. JAMA 323(14), 1406–1407 (2020)

    Article  Google Scholar 

  15. F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  Google Scholar 

  16. T. Davenport, R. Kalakota, The potential for artificial intelligence in healthcare. Future Healthcare J. 6(2), 94 (2019)

    Article  Google Scholar 

  17. S. Reddy, J. Fox, M.P. Purohit, Artificial intelligence-enabled healthcare delivery. J. R. Soc. Med. 112(1), 22–28 (2019)

    Article  Google Scholar 

  18. T.B. Alakus, I. Turkoglu, Detection of pre-epileptic seizure by using wavelet packet decomposition and artifical neural networks, in 2017 10th International Conference on Electrical and Electronics Engineering (ELECO) (IEEE, 2017), pp. 511–515

    Google Scholar 

  19. N. Memarian, S. Kim, S. Dewar, J. Engel Jr., R.J. Staba, Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput. Biol. Med. 64, 67–78 (2015)

    Article  Google Scholar 

  20. J. Yousefi, A. Hamilton-Wright, Characterizing emg data using machine-learning tools. Comput. Biol. Med. 51, 1–13 (2014)

    Article  Google Scholar 

  21. P. Karthick, D.M. Ghosh, S. Ramakrishnan, Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Comput. Methods Programs Biomed. 154, 45–56 (2018)

    Article  Google Scholar 

  22. M. Alfaras, M.C. Soriano, S. Ortín, A fast machine learning model for ecg-based heartbeat classification and arrhythmia detection. Front. Phys. 7, 103 (2019)

    Article  Google Scholar 

  23. C.A. Ledezma, X. Zhou, B. Rodriguez, P. Tan, V. Diaz-Zuccarini, A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG. PloS One 14(8), e0220294 (2019)

    Google Scholar 

  24. S. Kooraki, M. Hosseiny, L. Myers, A. Gholamrezanezhad, Coronavirus (covid-19) outbreak: what the department of radiology should know. J. Am. Coll. Radiol. (2020)

    Google Scholar 

  25. Y. Wang, Y. Wang, Y. Chen, Q. Qin, Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (covid-19) implicate special control measures. J. Med. Virol. 92(6), 568–576 (2020)

    Article  Google Scholar 

  26. T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, L. Xia, Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (covid-19) in china: a report of 1014 cases. Radiology 200642 (2020)

    Google Scholar 

  27. Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, W. Ji, Sensitivity of chest CT for covid-19: comparison to RT-PCR. Radiology 200432 (2020)

    Google Scholar 

  28. H. Zeng, C. Xu, J. Fan, Y. Tang, Q. Deng, W. Zhang, X. Long, Antibodies in infants born to mothers with covid-19 pneumonia. JAMA 323(18), 1848–1849 (2020)

    Google Scholar 

  29. D.A. Schwartz, An analysis of 38 pregnant women with covid-19, their newborn infants, and maternal-fetal transmission of sars-cov-2: maternal coronavirus infections and pregnancy outcomes. Arch. Pathol. Lab. Med. 144(7), 799–805 (2020)

    Article  Google Scholar 

  30. M. Li, P. Lei, B. Zeng, Z. Li, P. Yu, B. Fan, C. Wang, Z. Li, J. Zhou, S. Hu et al., Coronavirus disease (covid-19): spectrum of CT findings and temporal progression of the disease. Acad. Radiol. (2020)

    Google Scholar 

  31. F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, K. He, Y. Shi, D. Shen, Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev. Biomed. Eng. (2020)

    Google Scholar 

  32. H.S. Maghdid, A.T. Asaad, K.Z. Ghafoor, A.S. Sadiq, M.K. Khan, Diagnosing covid-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. arXiv:2004.00038 (2020)

  33. World Health Organization et al., Coronavirus disease 2019 (covid-19): situation report. 72 (2020)

    Google Scholar 

  34. D.D. Miller, E.W. Brown, Artificial intelligence in medical practice: the question to the answer? Am. J. Med. 131(2), 129–133 (2018)

    Article  Google Scholar 

  35. K.-H. Yu, A.L. Beam, I.S. Kohane, Artificial intelligence in healthcare. Nat. Biomed. Eng. 2(10), 719–731 (2018)

    Article  Google Scholar 

  36. A. Albahri, R.A. Hamid et al., Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (covid-19): A systematic review. J. Med. Syst. 44(7) (2020)

    Google Scholar 

  37. Z. Yang, Z. Zeng, K. Wang, S.-S. Wong, W. Liang, M. Zanin, P. Liu, X. Cao, Z. Gao, Z. Mai et al., Modified SEIR and AI prediction of the epidemics trend of covid-19 in China under public health interventions. J. Thorac. Dis. 12(3), 165 (2020)

    Article  Google Scholar 

  38. M. Alsalem, A. Zaidan, B. Zaidan, M. Hashim, O. Albahri, A. Albahri, A. Hadi, K. Mohammed, Systematic review of an automated multiclass detection and classification system for acute leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. J. Med. Syst. 42(11), 204 (2018)

    Article  Google Scholar 

  39. M. Alsalem, A. Zaidan, B. Zaidan, O. Albahri, A. Alamoodi, A. Albahri, A. Mohsin, K. Mohammed, Multiclass benchmarking framework for automated acute leukaemia detection and classification based on BWM and group-VIKOR. J. Med. Syst. 43(7), 212 (2019)

    Article  Google Scholar 

  40. A. Zaidan, B. Zaidan, M. Alsalem, O. Albahri, A. Albahri, M. Qahtan, Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput. Appl. 32(12), 8315–8366 (2020)

    Article  Google Scholar 

  41. A. Zaidan, B. Zaidan, O. Albahri, M. Alsalem, A. Albahri, Q.M. Yas, M. Hashim, A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health Technol. 8(4), 223–238 (2018)

    Article  Google Scholar 

  42. D. Li, D. Wang, J. Dong, N. Wang, H. Huang, H. Xu, C. Xia, False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: role of deep-learning-based CT diagnosis and insights from two cases. Korean J. Radiol. 21(4), 505–508 (2020)

    Article  Google Scholar 

  43. L.A. Wallis, Covid-19 severity scoring tool for low resourced settings. Afr. J. Emergency Med. (2020)

    Google Scholar 

  44. Y. Oh, S. Park, J.C. Ye, Deep learning covid-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging (2020)

    Google Scholar 

  45. F. Ucar, D. Korkmaz, Covidiagnosis-net: deep Bayes-squeezenet based diagnostic of the coronavirus disease 2019 (covid-19) from X-ray images. Med. Hypotheses 109761 (2020)

    Google Scholar 

  46. R.M. Pereira, D. Bertolini, L.O. Teixeira, C.N. Silla Jr, Y.M. Costa, Covid-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput. Methods Programs Biomed. 105532 (2020)

    Google Scholar 

  47. S.A. Harmon, T.H. Sanford, S. Xu, E.B. Turkbey, H. Roth, Z. Xu, D. Yang, A. Myronenko, V. Anderson, A. Amalou et al., Artificial intelligence for the detection of covid-19 pneumonia on chest CT using multinational datasets. Nat. Commun. 11(1), 1–7 (2020)

    Article  Google Scholar 

  48. L. Li, L. Qin, Z. Xu, Y. Yin, X.Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song et al., Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296(2) (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of ECE, NMAM Institute of Technology (VTU, Belagavi), Nitte, Karnataka, 574110, India

    M. J. Dileep Kumar & Prabha Niranjajn

  2. Department of Mechanical Engineering, NMAM Institute of Technology (VTU, Belagavi), Nitte, Karnataka, 574110, India

    G. Santhosh

  3. Department of CSE, NMAM Institute of Technology (VTU, Belagavi), Nitte, Karnataka, 574110, India

    G. R. Manasa

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  1. M. J. Dileep Kumar
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  2. G. Santhosh
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  3. Prabha Niranjajn
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  4. G. R. Manasa
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Corresponding author

Correspondence to M. J. Dileep Kumar .

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Editors and Affiliations

  1. Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka (NITK), Mangalore, India

    Pushparaj Shetty D.

  2. Department of MCA, NMAM Institute of Technology, Karkala, India

    Surendra Shetty

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Kumar, M.J.D., Santhosh, G., Niranjajn, P., Manasa, G.R. (2022). A Review on Effectiveness of AI and ML Techniques for Classification of COVID-19 Medical Images. In: Shetty D., P., Shetty, S. (eds) Recent Advances in Artificial Intelligence and Data Engineering. Advances in Intelligent Systems and Computing, vol 1386. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_14

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