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Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models

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Abstract

Classification of bacteria is essential in the medical diagnosis of infectious agents, their phylogenetic study, and their biotechnological exploitation for healthcare, food, industry, and agricultural sectors. Nevertheless, skilled experts and professional human-effort are necessary to identify and classify the bacteria manually. With the advancement of technology, now the task of recognizing details from digital stereomicroscopes is being performed by computers based on machine learning and computer vision technologies. Besides, machine learning methods include Deep Neural Networks (NN) has attained remarkable outcomes in the field of image classification recently. Furthermore, other machine learning methods except for NN methods already have acceptable performance. In this paper, we review the publications, which investigate the discrimination between bacteria genera and suborders based on macroscopic images via image processing and machine learning methods. The published research works in this regard are summarized, and the pros and cons of them are discussed. Moreover, the related databases and resources for this purpose are surveyed, and the lack of such data points in the global catalogue of microorganisms is criticized. In addition, in this paper, we have investigated an approach to automate the process of bacteria recognition and classification with the use of the Gabor transform and XGBoost classification method. We have used a dataset that includes microscopic images of three different Myxobacterial suborders. The trained model was able to recognize and classify all members of three different categories of bacteria, while the experimental results of prediction achieved an accuracy of around 91%, which has enhancement about 2% in term of accuracy.

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References

  1. Ahmed T, Md. Ferdous Wahid, Md. Jahid Hasan (2019) Combining deep convolutional neural network with support vector machine to classify microscopic bacteria images, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

  2. Balagurusamy V, Siu V, Kumar AD, Dureja S, Ligman J, Kudva P, Tong M, Dillenberger D (2019) Detecting and discriminating between different types of bacteria with a low-cost smartphone based optical device and neural network models. Proc. SPIE 11087, Biosensing and Nanomedicine XII:110870E doi: 10.1117/12

    Google Scholar 

  3. Chen T; Guestrin C (2016). XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13–17, 2016. ACM. pp. 785–794.

  4. ChulKim N, JooSo H (2018) Directional statistical Gabor features for texture classification. Pattern Recogn Lett 112(1):18–26

    Google Scholar 

  5. Deng L, Yu D (2014) Deep learning: methods and applications. Foundations and Trends®in Signal Processing 7:197–387

    Article  MathSciNet  Google Scholar 

  6. Dong N, Shank EA, Jojic V (2015) A deep framework for bacterial image segmentation and classification, the 6th ACM Conference.

  7. Hay EA, Parthasarathy R (2018) Performance of convolutional neural networks for the identification of bacteria in 3D microscopy datasets. PLoS Comput Biol 14(12):e1006628. https://doi.org/10.1371/journal.pcbi.1006628

    Article  Google Scholar 

  8. Huang L, Wu T (2018) Novel neural network application for bacterial colony classification. Theor Biol Med Model 15:22. https://doi.org/10.1186/s12976-018-0093-x

    Article  Google Scholar 

  9. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:2012

    Google Scholar 

  10. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. The 26th Annual Conference on Neural Information Processing Systems (NIPS), Nevada, USA; p. 1097–105

  11. Mohamad NA, Jusoh NA, Htike ZZ and Win SL (April 2014) Bacteria identification from microscopic morphology using Naïve Bayes, International journal of computer science, engineering and information technology, 4(2), .

    Google Scholar 

  12. Mohamed BA and Afify HM (2018), December. Automated classification of bacterial images extracted from digital microscope via bag of words model. In 2018 9th Cairo international biomedical engineering conference (CIBEC) (pp. 86-89). IEEE.

  13. Nasip ÖF and Zengin K (2018) Deep learning based bacteria classification. In 2018 2nd international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1–5). IEEE.

  14. No Author (2015) Bacteria images on Howmed. [Online]. Available: http://howmed.net/microbiologyhttps://doi.org/10.1117/12.2529829

  15. No Author (2015) Bacteria images on Microbiology-in-Pictures. [Online]. Available: https://www.microbiologyinpictures.com

  16. No Author (2015) Bacteria images on HOWMED. [Online]. Available: http://howmed.net/microbiology

  17. No Author (2015) Bacteria images on MICROBIOLOGY-IN-PICTURES. [Online]. Available: https://www.microbiologyinpictures.com

  18. No Author (2018) Bacteria images on Pixnio. [Online]. Available: https://pixnio.com/photos/science/microscopy-images

  19. No Author (2018) Bacteria images on MicrobIA Haemolysis Dataset. [Online]. Available: http://www.microbia.org/index.php/resources

  20. No Author (2018) Bacteria images on PIXNIO. [Online]. Available: https://pixnio.com/photos/science/microscopy-images

  21. Patsekin V, On S, Sturgis J, Bae E, Rajwa B, Patsekin A, Paul Robinson J (2019) Classification of Arcobacter species using variational autoencoders. Conference Paper, April

    Book  Google Scholar 

  22. Plichta A (2019) Methods of classification of the genera and species of Bacteria using decision tree. Journal of Telecommunications and Information Technology 4:72–84

    Google Scholar 

  23. Preetha V, Pandi Selvi P (2018) Identification of Bacteria using digital image processing. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) 5(3):2394–2320

    Google Scholar 

  24. Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET et al (2017) ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18:529

    Article  Google Scholar 

  25. Sajedi H, Mohammadipanah F, Panahi HKS (2018) An image analysis-aided method for redundancy reduction in the differentiation of identical Actinobacterial strains, Future Microbiology, 13(3), Future Medicine Ltd.

  26. Sajedi H, Panah FM, Rahimi AH (2019) Actinobacterial strains recognition by machine learning methods. Multimed Tools Appl 16(50):1–23

    Google Scholar 

  27. Sajedi H, Panah FM, Pashaie A (2019) Automated identification of Myxobacterial genera using the convolutional neural network. Sci Rep 9(18238):18238

    Article  Google Scholar 

  28. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682

    Article  Google Scholar 

  29. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to imageJ: 25 years of image analysis. Nat Methods 9:671–675

    Article  Google Scholar 

  30. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2019) Going deeper with convolutions, in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2015

  31. Talo M (2019) An automated deep learning approach for bacterial image classification, International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES2019), Apr 26-28, 2019 Alanya, Turkey

  32. Thompson CC, Amaral GR, Campeao M et al (2015) Microbial taxonomy in the post-genomic era: rebuilding from scratch? Arch Microbiol 197:359–370

    Article  Google Scholar 

  33. Treebupachatsakul T, Poomrittigul S (2019) Bacteria classification using image processing and deep learning, 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). JeJu, Korea (South), pp 1–3

    Google Scholar 

  34. TronnoloneI H, Gardner JM, SundstromI JF, Jiranek V, Oliver SG, Binder BJ (2018 Dec) TAMMiCol: tool for analysis of the morphology of microbial colonies. PLOS Computational Biology 14(12):e1006629

    Article  Google Scholar 

  35. Vanitha L and Venmathi AR (2011) Classification of medical images using support vector machine," in Proceedings of International Conference on Information and Network Technology, vol. 4.

  36. Vijaykumar V (2016) Classifying bacterial species using computer vision and machine learning. International Journal of Computer Applications 151(8):23–26

    Article  Google Scholar 

  37. Wahid MF, Ahmed T, Habib MA (2018) Classification of microscopic images of Bacteria using deep convolutional neural network, 10th International Conference on Electrical and Computer engineering (ICECE). Dhaka, Bangladesh, pp 217–220

    Google Scholar 

  38. Wahid MF, Hasan J and Alom S (2019) Deep convolutional NeuralNetwork for microscopic Bacteria image classification, 2019 5th International Conference on advances in Electrical engineering (ICAEE), Dhaka, Bangladesh

  39. Wang H, Shang S, Long L, Hu R, Wu Y, Chen N, Zhang S, Cong F, Lin S (2018) Biological image analysis using deep learning-based methods: literature review. Digit Med 4:157–165

    Article  Google Scholar 

  40. Wang H et al. (2020) Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning, In Press.

  41. Wu L, Sun Q, Sugawara H, Yang S, Zhou Y, McCluskey K, Vasilenko A, Suzuki K-I, Ohkuma M, Lee Y, Robert V, Ingsriswang S, Guissart F, Philippe D, Ma J (2013) Global catalogue of microorganisms (gcm): a comprehensive database and information retrieval, analysis, and visualization system for microbial resources. BMC Genomics 14:933–952

    Article  Google Scholar 

  42. Zhang W, Li R, Zeng T, Sun Q, Kumar S, Ye J, et al. Deep model based transfer and multi-task learning for biological image analysis. IEEE Transactions on Big Data; 2016. p. 1475–1484.

  43. Zieliński B, Plichta A, Misztal K, Spurek P, Brzychczy-Włoch M, Ochońska D (2017) Deep learning approach to bacterial colony classification. PloS one 12(9):e 0184554

    Article  Google Scholar 

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Authors and Affiliations

  1. Pharmaceutical Biotechnology Lab, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, 14155-6455, Iran

    Hedieh Sajedi

  2. Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, 14155-6455, Iran

    Fatemeh Mohammadipanah

  3. Mathematical and Computer Science Department, Amirkabir University of Technology, Tehran, Iran

    Ali Pashaei

Authors
  1. Hedieh Sajedi
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  2. Fatemeh Mohammadipanah
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  3. Ali Pashaei
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Correspondence to Hedieh Sajedi or Fatemeh Mohammadipanah.

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Sajedi, H., Mohammadipanah, F. & Pashaei, A. Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models. Multimed Tools Appl 79, 32711–32730 (2020). https://doi.org/10.1007/s11042-020-09284-9

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  • DOI: https://doi.org/10.1007/s11042-020-09284-9

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