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An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning

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Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis

Abstract

In December 2019, a member of the Coronaviridae family crossed the barriers between species and hit humans for the first time. Associated with Severe Acute Respiratory Syndrome (SARS) the virus was named SARS-CoV-2. The new coronavirus is responsible for 2019 coronavirus disease (Covid-19). Until August 20, the world has counted more than 22.5 million Covid-19 positive cases, about 14.4 million recovered cases, and more than 790 thousand deaths. In this study, we propose an automatic system for Covid-19 diagnosis using machine learning techniques and CT X-ray images named IKONOS-CT. The main idea is that healthcare professionals can upload the CT image into the system to be analyzed by an intelligent system. Then IKONOS-CT will provide a binary classification, differentiating Covid-19 patients from non-Covid-19 ones. For classification tasks we performed 25 experiments for the following classifiers: Multilayer Perceptron, Support Vector Machines, Random Tree and Random Forest, and Bayesian Networks. The best overall performance was found using Haralick as feature extractor and SVM with polynomial kernel of exponent 3. We found average results of accuracy of 96.994 ± 1.375%, kappa index of 0.940 ± 0.028, sensitivity/recall of 0.952 ± 0.024, precision of 0.987 ± 0.014, specificity of 0.987 ± 0.014, and area under ROC of 0.970 ± 0.014. By using a computationally low-cost method, based on Haralick extractor, it was possible to achieve high diagnosis performance. Indicating that an effective path for Covid-19 diagnosis may be found by combining AI for CT images classification and clinical analysis.

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References

  1. Ackermann, M., Verleden, S. E., Kuehnel, M., Haverich, A., Welte, T., Laenger, F., …et al. (2020). Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in covid-19. New England Journal of Medicine, 383, 120–128.

    Google Scholar 

  2. Apostolopoulos, I., Aznaouridis, S., & Tzani, M. (2020). Extracting possibly representative covid-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. Preprint. arXiv:2004.00338.

    Google Scholar 

  3. 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, 635–640.

    Google Scholar 

  4. Azevedo, W. W., Lima, S. M. L., Fernandes, I. M. M., Rocha, A. D. D., Cordeiro, F. R., da Silva-Filho, A. G., & dos Santos, W. P. (2015). Morphological extreme learning machines applied to detect and classify masses in mammograms. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8).

    Google Scholar 

  5. Bai, H. X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J. W., Tran, T. M. L., …others (2020). Performance of radiologists in differentiating covid-19 from viral pneumonia on chest CT. Radiology, 296, E46–E54.

    Google Scholar 

  6. Barbosa, V. A. F., Santana, M. A., Andrade, M. K. S., Lima, R. C. F., & Santos, W. P. (2020). Deep-wavelet neural networks for breast cancer early diagnosis using mammary termographies. In H. Das, C. Pradhan, & N. Dey (Eds.), Deep learning for data analytics: Foundations, biomedical applications, and challenges (1st ed.). Academic Press

    Google Scholar 

  7. Beeching, N. J., Fletcher, T. E., & Beadsworth, M. B. J. (2020). Covid-19: testing times. BMJ, 369. Retrieved from https://www.bmj.com/content/369/bmj.m1403

  8. Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N., …others (2020). Chest CT findings in coronavirus disease-19 (covid-19): relationship to duration of infection. Radiology, 295(3), 200463.

    Google Scholar 

  9. Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., …Raja, N. S. M. (2020). Deep-learning framework to detect lung abnormality–a study with chest x-ray and lung ct scan images. Pattern Recognition Letters, 129, 271–278.

    Google Scholar 

  10. Borges, L. (2016). Medidas de acurácia diagnóstica na pesquisa cardiovascular. International Journal of Cardiovascular Science, 29(3), 218–222.

    Google Scholar 

  11. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 144–152).

    Google Scholar 

  12. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Google Scholar 

  13. Burog, A. I. L. D., Yacapin, C. P. R. C., Maglente, R. R. O., Macalalad-Josue, A. A., & Uy, E. J. B. (2020). Should IgM/IgG rapid test kit be used in the diagnosis of COVID-19? Asia Pacific Center for Evidence Based Healthcare, 2020(04), 1–12.

    Google Scholar 

  14. Caruso, D., Zerunian, M., Polici, M., Pucciarelli, F., Polidori, T., Rucci, C., …Laghi, A. (2020). Chest CT features of COVID-19 in Rome, Italy. Radiology, 296(2), E79–E85.

    Google Scholar 

  15. Cheng, J., & Greiner, R. (2001). Learning bayesian belief network classifiers: Algorithms and system. Advances in Artificial Intelligence, 2056(1), 141–151.

    Google Scholar 

  16. Connors, J. M., & Levy, J. H. (2020). Covid-19 and its implications for thrombosis and anticoagulation. Blood, The Journal of the American Society of Hematology, 135(23), 2033–2040.

    Google Scholar 

  17. Cordeiro, F. R., Santos, W. P., & Silva-Filho, A. G. (2016). A semi-supervised fuzzy growcut algorithm to segment and classify regions of interest of mammographic images. Expert Systems with Applications, 65, 116–126.

    Google Scholar 

  18. Cordeiro, F. R., Santos, W. P. d., & Silva-Filho, A. G. (2017). Analysis of supervised and semi-supervised growcut applied to segmentation of masses in mammography images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 5(4), 297–315.

    Google Scholar 

  19. Coronavirus disease (covid-19) pandemic [Computer software manual]. (2020). Retrieved from www.who.int/emergencies/diseases/novel-coronavirus-2019. Last accessed: 28 April 2020.

  20. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Google Scholar 

  21. de Lima, S. M., da Silva-Filho, A. G., & dos Santos, W. P. (2014). A methodology for classification of lesions in mammographies using Zernike moments, ELM and SVM neural networks in a multi-kernel approach. In 2014 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 988–991).

    Google Scholar 

  22. de Lima, S. M., da Silva-Filho, A. G., & dos Santos, W. P. (2016). Detection and classification of masses in mammographic images in a multi-kernel approach. Computer Methods and Programs in Biomedicine, 134, 11–29.

    Google Scholar 

  23. de Santana, M. A., Pereira, J. M. S., da Silva, F. L., Lima, N. M. d., de Sousa, F. N., de Arruda, G. M. S., …Santos, W. P. (2018). Breast cancer diagnosis based on mammary thermography and extreme learning machines. Research on Biomedical Engineering, 34, 45–53.

    Google Scholar 

  24. de Vasconcelos, J., dos Santos, W., & de Lima, R. (2018). Analysis of methods of classification of breast thermographic images to determine their viability in the early breast cancer detection. IEEE Latin America Transactions, 16(6), 1631–1637.

    Google Scholar 

  25. Döhla, M., Boesecke, C., Schulte, B., Diegmann, C., Sib, E., Richter, E., …others (2020). Rapid point-of-care testing for SARS-CoV-2 in a community screening setting shows low sensitivity. Public Health, 182, 170–172.

    Google Scholar 

  26. Egner, W., Beck, D. C. C., Davis, G., Dayan, C., El-shanawany, T., Griffiths, E., …others (2020). Statement from RCPath’s immunology specialty advisory committee on COVID-19/SARS CoV2 antibody evaluation. Institute of Clinical Epidemiology, National Institutes of Health-UP Manila and Asia-Pacific Center for Evidence Based Healthcare Inc.

    Google Scholar 

  27. Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology, 296(2), E115–E117.

    Google Scholar 

  28. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.

    Google Scholar 

  29. Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P., Zhang, H., Ji, W., …Siegel, E. (2020). Rapid AI development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv.

    Google Scholar 

  30. Guo, L., Ren, L., Yang, S., Xiao, M., Chang, D., Yang, F., …Wang, J. (2020). Profiling early humoral response to diagnose novel coronavirus disease (COVID-19). Clinical Infectious Diseases, 71(15), 778–785.

    Google Scholar 

  31. Hani, C., Trieu, N. H., Saab, I., Dangeard, S., Bennani, S., Chassagnon, G., & Revel, M.-P. (2020). Covid-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagnostic and Interventional Imaging, 101(5), 263–268.

    Google Scholar 

  32. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621.

    Google Scholar 

  33. Haykin, S. (2001). Neural networks: principles and practice. Bookman, 11, 900.

    Google Scholar 

  34. Hoffman, T., Nissen, K., Krambrich, J., Rönnberg, B., Akaberi, D., Esmaeilzadeh, M., …Lundkvist, Å. (2020). Evaluation of a covid-19 IGM and IGG rapid test; an efficient tool for assessment of past exposure to SARS-COV-2. Infection Ecology & Epidemiology, 10(1), 1754538.

    Google Scholar 

  35. Kan, C., & Srinath, M. D. (2001). Combined features of cubic b-spline wavelet moments and zernike moments for invariant character recognition. In Proceedings International Conference on Information Technology: Coding and Computing (pp. 511–515).

    Google Scholar 

  36. Kanazawa, K., Niki, N., Satoh, H., Ohmatsu, H., & Moriyama, N. (1994). Computer assisted diagnosis of lung cancer using helical X-ray CT. In Proceedings of IEEE Workshop on Biomedical Image Analysis (pp. 261–267).

    Google Scholar 

  37. Leisman, D. E., Deutschman, C. S., & Legrand, M. (2020). Facing COVID-19 in the ICU: vascular dysfunction, thrombosis, and dysregulated inflammation. Intensive Care Medicine, 46(6), 1105–1108.

    Google Scholar 

  38. Lerner, B., Levinstein, M., Rosenberg, B., Guterman, H., Dinstein, L., & Romem, Y. (1994). Feature selection and chromosome classification using a multilayer perceptron neural network. In Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94) (Vol. 6, pp. 3540–3545).

    Google Scholar 

  39. Li, K., Wu, J., Wu, F., Guo, D., Chen, L., Fang, Z., & Li, C. (2020a). The clinical and chest ct features associated with severe and critical covid-19 pneumonia. Investigative Radiology, 55, 327–331.

    Google Scholar 

  40. Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., …Xia, J. (2020b). Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology, 296(2), E65–E71.

    Google Scholar 

  41. Li, M., Lei, P., Zeng, B., Li, Z., Yu, P., Fan, B., …Liu, H. (2020c). Coronavirus disease (covid-19): Spectrum of ct findings and temporal progression of the disease. Academic Radiology, 27(5), 603–608.

    Google Scholar 

  42. Li, X., Geng, M., Peng, Y., Meng, L., & Lu, S. (2020d). Molecular immune pathogenesis and diagnosis of COVID-19. Journal of Pharmaceutical Analysis, 10(2), 102–108.

    Google Scholar 

  43. Li, Y., & Xia, L. (2020). Coronavirus disease 2019 (covid-19): role of chest CT in diagnosis and management. American Journal of Roentgenology, 214(6), 1280–1286.

    Google Scholar 

  44. Li, Z., Yi, Y., Luo, X., Xiong, N., Liu, Y., Li, S., …others (2020e). Development and clinical application of a rapid IGM-IGG combined antibody test for SARS-COV-2 infection diagnosis. Journal of Medical Virology, 92, 1518–1524.

    Google Scholar 

  45. Lima, S., Azevedo, W., Cordeiro, F., Silva-Filho, A., & Santos, W. (2015). Feature extraction employing fuzzy-morphological decomposition for detection and classification of mass on mammograms. In Conference Proceedings:…Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference (Vol. 2015, pp. 801–804).

    Google Scholar 

  46. Lin, D., Liu, L., Zhang, M., Hu, Y., Yang, Q., Guo, J., …et al. (2020). Evaluations of serological test in the diagnosis of 2019 novel coronavirus (SARS-CoV-2) infections during the COVID-19 outbreak. medRxiv.

    Google Scholar 

  47. Liu, Y., Liu, Y., Diao, B., Ren, F., Wang, Y., Ding, J., & Huang, Q. (2020). Diagnostic indexes of a rapid IGG/IGM combined antibody test for SARS-COV-2. medRxiv.

    Google Scholar 

  48. Magro, C., Mulvey, J. J., Berlin, D., Nuovo, G., Salvatore, S., Harp, J., …Laurence, J. (2020). Complement associated microvascular injury and thrombosis in the pathogenesis of severe covid-19 infection: a report of five cases. Translational Research, 220, 1–13.

    Google Scholar 

  49. Marietta, M., Ageno, W., Artoni, A., De Candia, E., Gresele, P., Marchetti, M., …Tripodi, A. (2020). Covid-19 and haemostasis: a position paper from Italian society on thrombosis and haemostasis (SISET). Blood Transfusion, 18(3), 167.

    Google Scholar 

  50. Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. Preprint. arXiv: 2003.10849.

    Google Scholar 

  51. Okba, N. M., Muller, M. A., Li, W., Wang, C., GeurtsvanKessel, C. H., Corman, V. M., …et al. (2020). SARS-CoV-2 specific antibody responses in covid-19 patients. medRxiv.

    Google Scholar 

  52. Patel, R., Babady, E., Theel, E. S., Storch, G. A., Pinsky, B. A., St George, K., …Bertuzzi, S. (2020). Report from the American Society for Microbiology COVID-19 International Summit, 23 March 2020: Value of diagnostic testing for SARS–CoV-2/COVID-19. mBio, 11(2), e00722–20.

    Google Scholar 

  53. Pereira, J. M. S., Santana, M. A., Lima, R. C. F., Lima, S. M. L., & Santos, W. P. (2020a). Method for classification of breast lesions in thermographic images using elm classifiers. In W. P. dos Santos, M. A. de Santana, & W. W. A. da Silva (Eds.), Understanding a cancer diagnosis (1st ed., pp. 117–132). Nova Science.

    Google Scholar 

  54. Pereira, J. M. S., Santana, M. A., Lima, R. C. F., & Santos, W. P. (2020b). Lesion detection in breast thermography using machine learning algorithms without previous segmentation. In W. P. dos Santos, M. A. de Santana, & W. W. A. da Silva (Eds.), Understanding a cancer diagnosis (1st ed., pp. 81–94). Nova Science.

    Google Scholar 

  55. Pereira, J. M. S., Santana, M. A., Silva, W. W. A., Lima, R. C. F., Lima, S. M. L., & Santos, W. P. (2020c). Dialectical optimization method as a feature selection tool for breast cancer diagnosis using thermographic images. In W. P. dos Santos, M. A. de Santana, & W. W. A. da Silva (Eds.), Understanding a cancer diagnosis (1st ed., pp. 95–118). Nova Science.

    Google Scholar 

  56. Phung, S. L., Bouzerdoum, A., & Chai, D. (2005). Skin segmentation using color pixel classification: analysis and comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), 148–154.

    Google Scholar 

  57. Rodrigues, A. L., de Santana, M. A., Azevedo, W. W., Bezerra, R. S., Barbosa, V. A., de Lima, R. C., & dos Santos, W. P. (2019). Identification of mammary lesions in thermographic images: feature selection study using genetic algorithms and particle swarm optimization. Research on Biomedical Engineering, 35(3), 213–222.

    Google Scholar 

  58. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.

    Google Scholar 

  59. Santana, M. A., Pereira, J. M. S., Lima, R. C. F., & Santos, W. P. (2020). Breast lesions classification in frontal thermographic images using intelligent systems and moments of haralick and zernike. In W. P. dos Santos, M. A. de Santana, & W. W. A. da Silva (Eds.), Understanding a cancer diagnosis (1st ed., pp. 65–80). Nova Science.

    Google Scholar 

  60. Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (Covid-19) based on deep features. Preprints, 2020030300, 2020.

    Google Scholar 

  61. Silva, W. W. A., Santana, M. A., Silva Filho, A. G., Lima, S. M. L., & Santos, W. P. (2020). Morphological extreme learning machines applied to the detection and classification of mammary lesions. In T. K. Gandhi, S. Bhattacharyya, S. De, D. Konar, & S. Dey (Eds.), Advanced machine vision paradigms for medical image analysis. Elsevier.

    Google Scholar 

  62. Tang, Y. W., Schmitz, J. E., Persing, D. H., & Stratton, C. W. (2020). Laboratory diagnosis of COVID-19: current issues and challenges. Journal of Clinical Microbiology, 58(6), e00512–20.

    Google Scholar 

  63. Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., …Xu, B. (2020). A deep learning algorithm using ct images to screen for corona virus disease (COVID-19). medRxiv.

    Google Scholar 

  64. Yamamoto, S., Matsumoto, M., Tateno, Y., Iinuma, T., & Matsumoto, T. (1996). Quoit filter-a new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in X-ray ct. In Proceedings of 13th International Conference on Pattern Recognition (Vol. 2, pp. 3–7).

    Google Scholar 

  65. Zhao, J., Zhang, Y., He, X., & Xie, P. (2020). Covid-CT-dataset: a CT scan dataset about covid-19. arXiv preprint arXiv:2003.13865.

    Google Scholar 

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Acknowledgements

The authors are grateful to the Federal University of Pernambuco and the Brazilian research agencies FACEPE, CAPES, and CNPq, for the partial financial support of this research.

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Authors and Affiliations

  1. Graduate Program in Computer Engineering, Polytechnique School of the University of Pernambuco, Recife, Brazil

    Maíra Araújo de Santana, Juliana Carneiro Gomes, Clarisse Lins de Lima, Jonathan Bandeira & Mêuser Jorge Silva Valença

  2. Academic Unit of Serra Talhada, Rural Federal University of Pernambuco, Serra Talhada, Brazil

    Valter Augusto de Freitas Barbosa & Ricardo Emmanuel de Souza

  3. Department of Biomedical Engineering, Federal University of Pernambuco, Recife, Brazil

    Aras Ismael Masood

  4. Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq

    Wellington P. dos Santos

  5. Department of Biomedical Engineering, Federal University of Pernambuco, Recife, Pernambuco, Brazil

    Wellington P. dos Santos

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  1. Maíra Araújo de Santana
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  6. Mêuser Jorge Silva Valença
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  9. Wellington P. dos Santos
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Correspondence to Wellington P. dos Santos .

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Editors and Affiliations

  1. School of Computer Science and Engg., Orissa Engineering College, Bhubaneswar-Khordha, Odisha, India

    Subhendu Kumar Pani

  2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University (Erstwhile North Orissa University), Takatpur, Baripada, Odisha, India

    Sujata Dash

  3. Biomedical Engineering, Federal University of Pernambuco, Recife, Pernambuco, Brazil

    Wellington P. dos Santos

  4. Comp Science, Mathematics and Science, St. John’s University, Jamaica, NY, USA

    Syed Ahmad Chan Bukhari

  5. Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

    Francesco Flammini

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de Santana, M.A. et al. (2022). An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning. In: Pani, S.K., Dash, S., dos Santos, W.P., Chan Bukhari, S.A., Flammini, F. (eds) Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-79753-9_15

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