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Application of Fuzzy Convolutional Neural Network for Disease Diagnosis: A Case of Covid-19 Diagnosis Through CT Scanned Lung Images

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Tracking and Preventing Diseases with Artificial Intelligence

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 206))

Abstract

The medical diagnosis can be enhanced by intelligent and automatic diagnosis through advances in Information and Communication Technologies (ICT) during the Covid-19 pandemic. This chapter discusses the current scenario, fundamental concepts, and existing solutions for diagnosing corona based diseases and their limitations. The chapter presents a generic and hybrid intelligent architecture for disease diagnosis. The architecture considers CT scanned images along with other fuzzy parameters and classifies the images into various disease categories using a convolutional neural network. The fuzzy convolutional neural network has experimented on 100 CT scanned images of lungs with additional fuzzy symptoms to prove the architecture's utility. The working of the convolutional layer, pooling layer, fully connected layer, fuzzy membership functions, and training data sets used in the experiment are discussed in detail in this chapter. The results are analyzed and presented graphically with improvement in accuracy, sensitivity, and precision. The chapter concludes with applications of the architecture for other disease diagnoses using radiology images and also discusses limitations and future work enhancement.

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Notes

  1. 1.

    https://www.kaggle.com.

  2. 2.

    https://github.com/.

  3. 3.

    https://www.nlm.nih.gov/research/visible/getting_data.html.

  4. 4.

    https://neurohive.io/en/popular-networks/u-net/.

References

  1. Abiyev, R.H., Ma’aitah, M.K.: Deep convolutional neural networks for chest diseases detection. J. Healthcare Eng. (2018)

    Google Scholar 

  2. Akerkar, R.A., Sajja, P.: Knowledge-Based Systems. Jones & Bartlett Publishers, Sudbury, MA, USA (2010)

    Google Scholar 

  3. Aleksander, I., Morton, H.: An Introduction to Neural Computing. International Thomson Computer Press, Boston (1995)

    Google Scholar 

  4. Al-Shayea, Q.: Artificial neural networks in medical diagnosis. Int. J. Comput. Sci. Issues 8(2), 150–154 (2011)

    Google Scholar 

  5. Andre, E., Brett, K., Roberto A.N., Ko, J., Swetter, A.M., Balu, H.M., Sebastian, T.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

    Google Scholar 

  6. Bernheim, A., Huang, M., et. al.: Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Relationship to duration of infection (2020)

    Google Scholar 

  7. Cohen, J.P., Morrison, P., Dao, L.: COVID-19 image data collection. Retrieved from https://arxiv.org/abs/2003.11597 (2020)

  8. da Silva, G.L., da Silva Neto, O.P., Silva, A.C.: Lung nodules diagnosis based on evolutionary convolutional neural network. Multimed. Tools Appl. 76, 19039–19055 (2017)

    Article  Google Scholar 

  9. Er, O., Yumusak, N., Temurtas, F.: Chest diseases diagnosis using artificial neural networks. Expert Syst. Appl. 37(12), 7648–7655 (2010)

    Article  Google Scholar 

  10. Feigenbaum, E.A., Buchanan, B.G.: DENDRAL and Meta-DENDRAL: roots of knowledge systems and expert system applications. Artif. Intell. 59, 233–240 (1993)

    Article  Google Scholar 

  11. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)

    Google Scholar 

  12. Hsu, M., Chien, Y., Wang, W.: A convolutional fuzzy neural network architecture for object classification with small training database. Int. J. Fuzzy Syst. 22, 1–10 (2020)

    Article  Google Scholar 

  13. Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K.: Abnormality detection and localization in chest x-rays using deep convolutional neural networks. https://arxiv.org/abs/1705.09850 (2017)

  14. Kaur, M., Gianey, H., Sabharwal, M., Singh, D.: Multiobjective differential evolution based random forest for e-health applications. Mod. Phys. Lett. B 33(5), 1950022 (2019)

    Article  Google Scholar 

  15. Korshunova, K.P.: A convolutional fuzzy neural network for image classification. In: 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC), pp. 1–4. Vladivostok (2018)

    Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classifcation with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  17. Lalmuanawma, S., Hussain, J., Chhakchhuak, L.: Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review. Chaos Solitons Fractals 139 (2020)

    Google Scholar 

  18. Litjens, G., Kooi, T., Bejnordi, E.B.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  19. Ma’aitah, M.K., Abiyev, R., Bus, I.J.: Intelligent classification of liver disorder using fuzzy neural system. Int. J. Adv. Comput. Sci. Appl. 8(12), 25–31 (2017)

    Google Scholar 

  20. Melle, W.: MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Int. J. Man Mach. Stud. 10(3), 313–322 (1978)

    Article  Google Scholar 

  21. Moeskops, P., Viergever, M., Mendrik, A., De Vries, L., Benders, M., Isqum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)

    Article  Google Scholar 

  22. Mohanty, S., Hughes, D., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)

    Google Scholar 

  23. Nguyen, T.-L., Kavuri, S., Lee, M.: A fuzzy convolutional neural network for text sentiment analysis. J. Intell. Fuzzy Syst. Special Section Green Human Inf. Technol. 35(6), 6025–6034 (2018)

    Google Scholar 

  24. Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U., Yildirim, O., Acharya, R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121 (2020)

    Google Scholar 

  25. Pattrapisetwong, P., Chiracharit, W.: Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding. In: Proceedings of 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Manchester, UK: IEEE. Retrieved 2020 (2016)

    Google Scholar 

  26. Ramcharan, A., Baranowsk, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.: Deep learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017)

    Article  Google Scholar 

  27. Ravi, K.S., Heang-Ping, C., Lubomir, M.H., Mark, A.H., Caleb, R., Kenny, C.: Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys. Med. Biol. 63(9) (2018)

    Google Scholar 

  28. Sajja, P.: Application of fuzzy user's profile for mining reusable e-learning repositories on web through lightweight mobile agent. In: Yan, L. (ed.) Handbook of Research on Innovative Database Query Processing Techniques, pp. 500–521. IGI Global, Hershey, PA, USA (2015)

    Google Scholar 

  29. Sajja, P.S.: Illustrated Computational Intelligence. Springer, Singapore, Singapore (2020)

    Google Scholar 

  30. Sajja, P., Akerkar, R.: Deep learning for big data analytics. In: Banati, H., Mehta, S., Kaur, P. (eds.) Nature-Inspired Algorithms for Big Data Frameworks, pp. 1–21. IGI Global Book Publishing, Hershey, PA, USA (2018)

    Google Scholar 

  31. Sethy, P., Behera, S.: Detection of coronavirus disease (COVID-19) based on deep features. Preprints 2020, 2020030300 (2020)

    Google Scholar 

  32. Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., Shi, Y.: Lung infection quantification of COVID-19 in CT images with deep learning. Retrieved from https://arxiv.org/abs/2003.04655 (2020)

  33. Vaishya, R., Javaid, M., Khan, I., Haleem, A.: Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr. 14(4), 337–339 (2020)

    Article  Google Scholar 

  34. Venugopal, V.K., Mahajan, V., Rajan, S., Agarwal, V.K., Rajan, R., Syed, S., Mahajan, H.: A systematic meta-analysis of CT features of COVID-19: lessons from radiology. Retrieved from https://www.medrxiv.org/content/10.1101/2020.04.04.20052241v1 (2020)

  35. Wang, L., Wong, A.: COVID-net: a tailored deep convolutional neural network design for detection of COVID-19. Cases from chest radiography images. In Arxiv (2020)

    Google Scholar 

  36. Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., Xu, B.: A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). Retrieved from https://www.medrxiv.org/content/10.1101/2020.02.14.20023028v5 (2020)

  37. WHO.: Retrieved December 24, 2020, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (2020)

  38. Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., Lang, G., Wu, W.: Deep learning system to screen coronavirus disease 2019 pneumonia. Retrieved from https://arxiv.org/ftp/arxiv/papers/2002/2002.09334.pdf (2020)

  39. Yadav, S.S., Jadhav, S.M.: Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data 6(113) (2019)

    Google Scholar 

  40. Yadav, S., Jadhav, S., Nagrale, S., Patil, N.: Application of machine learning for the detection of heart disease. In: 2nd International Conference on Innovative Mechanisms for Industry Applications. IEEE, Bengaluru (2020)

    Google Scholar 

  41. Yamashita, R., Nishio, M., Do, R.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018)

    Article  Google Scholar 

  42. Yoon, S.H., Lee, K.H., Kim, J.Y., Lee, Y.K., Ko, H., Kim, K.H., Park, C.M., Kim, Y.H.: Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J. Radiol. 21(4), 494–500 (2020)

    Google Scholar 

  43. Yosuke, T., Fumio, O.: How convolutional neural networks diagnose plant disease. Plant Phenom. 9237136, 1–14 (2019)

    Google Scholar 

  44. Zadeh, A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Sardar Patel University, Vallabh Vidyanagar, Anand, India

    Priti Srinivas Sajja

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  1. Priti Srinivas Sajja
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Corresponding author

Correspondence to Priti Srinivas Sajja .

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Editors and Affiliations

  1. Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Surat, Gujarat, India

    Mayuri Mehta

  2. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Department of Computer Engineering, G. H. Patel College of Engineering and Technology, Charutar Vidya Mandal University, Vallabh Vidyanagar, Gujarat, India

    Maulika Patel

  4. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

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Sajja, P.S. (2022). Application of Fuzzy Convolutional Neural Network for Disease Diagnosis: A Case of Covid-19 Diagnosis Through CT Scanned Lung Images. In: Mehta, M., Fournier-Viger, P., Patel, M., Lin, J.CW. (eds) Tracking and Preventing Diseases with Artificial Intelligence. Intelligent Systems Reference Library, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-030-76732-7_8

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