Multimedia Tools and Applications

, Volume 78, Issue 14, pp 20285–20307 | Cite as

Actinobacterial strains recognition by Machine learning methods

  • Hedieh SajediEmail author
  • Fatemeh MohammadipanahEmail author
  • Seyyed Amir Hosein Rahimi


Recognition of actinobacterial species on solid culture plates is an error-prone and time-consuming process which retard all the down-stream process after the isolation step. In this paper, we propose an optimized solution for the mentioned problem by using machine learning and image processing algorithms to diminish the cost and time and increase the accuracy of the detection. Three methods are compared in this paper for actinobacterial strains recognition. In the first method, two-level wavelet transform is applied on images of actinobacterial strains and statistical texture features are computed from wavelet subbands. Furthermore, some statistical color features are calculated from color information. In consequence, Principle Component Analysis (PCA) is employed for dimension reduction and finally, a Multi-Layer Perceptron (MLP) neural network was used for classification. In the second method, a Convolution Neural Network (CNN) is used to extract the features automatically. In the third method, transfer learning is employed for feature extraction and classfication. The first method is evaluated on two databases, UTMC.V1.DB and UTMC.V2.DB, and the accuracy obtained between 80.8% to 80.1%, respectively. Employing CNN as the feature extractor improved the accuracy about 4%. Transfer learnig results 85.96% accuracy on the UTMC.V2.DB and 91.90% on the subclasses of UTMC.V2.DB. The experiments have shown that using transfer learning of DCNN of type ResNet has better performance compared to the pervious methods. The proposed methods are universal and can be used for recognition of other circle-like colony-shape microorganisms. In particular, giving limited and unbalanced training data, which is a common failure in biological data sets, the proposed methods harbor remarkable accuracy. The data augmentation methods showed to be efficient and practical for the current purpose along with being easy to be implemented and integrated.


Actinobacterial strains Colony features Image processing Deep neural network Transfer learning ResNet 


Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. 1.
    Affonso C, Debiaso Rossi A, Vieira F, De Carvalho A (2017) Deep learning for biological image classification. Expert Syst Appl 85:114–122CrossRefGoogle Scholar
  2. 2.
    Arrigoni S, Turra G, Signoroni S (2017) Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study. Comput Biol Med 88:60–71CrossRefGoogle Scholar
  3. 3.
    Bahrami M, Sajedi H (2019) Image concept detection in imbalanced datasets with ensemble of convolutional neural networks. Intelligent Data Analysis. In PressGoogle Scholar
  4. 4.
    Banada PP, Huff K, Bae E, Rajwa B, Aroonnual A, Bayraktar B, Adil A, PaulRobinson J, Hirleman ED, Bhunia AK (2009) Label-free detection of multiple bacterial pathogens using light-scattering sensor. Biosens Bioelectron 24(6):1685–1692CrossRefGoogle Scholar
  5. 5.
    Cardona D, Nedjah N, Mourelle L (2017) Online phoneme recognition using multi-layer perceptron networks combined with recurrent non-linear autoregressive neural networks with exogenous inputs. Neurocomputing. 265:78–90CrossRefGoogle Scholar
  6. 6.
    Chiang P, Tseng M, He Z, Li C (2015) Automated counting of bacterial colonies by image analysis. J Microbiol Methods 108:74–82CrossRefGoogle Scholar
  7. 7.
    Dopheide A, Lear G, He Z, Zhou J, Lewis GD (2015) Functional Gene Composition, Diversity and Redundancy in Microbial Stream Biofilm Communities. PLoS One 10(4):e0123179CrossRefGoogle Scholar
  8. 8.
    Ferrari A, Lombardi S, Signoroni A (2017) Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging. Pattern Recogn 61:629–640CrossRefGoogle Scholar
  9. 9.
    Ferrari A, Signoroni A (2014) Multistage classification for bacterial colonies recognition on solid agar images. 2014 IEEE International Conference on Imaging Systems and Techniques (IST). pp. 101–106Google Scholar
  10. 10.
    Gu J, Wan Z, Kuen J et al (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377CrossRefGoogle Scholar
  11. 11.
    Hayatab K, Qazi T (2017) Forgery detection in digital images via discrete wavelet and discrete cosine transforms. Comput Electr Eng 62:448–458CrossRefGoogle Scholar
  12. 12.
    Isler Y (2016) Discrimination of systolic and diastolic dysfunctions using multi-layer perceptron in heart rate variability analysis. Comput Biol Med 76:113–119CrossRefGoogle Scholar
  13. 13.
    Kaiming H, Zhang X, Ren S (2015) Deep Residual Learning for Image Recognition, Microsoft Research, eprint arXiv:1512.03385
  14. 14.
    Kingma DP, Ba J, (2015) Adam: a method for stochastic optimization. 3rd International Conference for Learning Representations, San DiegoGoogle Scholar
  15. 15.
    Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet Classification with Deep Convolutional Neural Networks, Part of: Advances in Neural Information Processing Systems 25 (NIPS 2012)Google Scholar
  16. 16.
    LeCun Y, Kavukcuoglu K, Farabet C (2010) convolutional networks and applications in vision. International Symposium on Circuits and Systems (ISCAS)Google Scholar
  17. 17.
    Li Q, Zhou X, Gu A, Li Z, Liang RZ (2018) Nuclear norm regularized convolutional Max Pos@Top machine. Neural Comput & Applic 30:463–472CrossRefGoogle Scholar
  18. 18.
    Liang RZ, Shi L, Wang H, Meng J, Wang JJY, Sun Q, Gu Y (2016) Optimizing Top precision performance measure of content based image retrieval by learning similarity function. 2016 23st International Conference on Pattern Recognition (ICPR)Google Scholar
  19. 19.
    Lim C, Paramesran R, Jassima WA, Yu YP, Ngan KN (2016) Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude. Journal of the Franklin Institute 353:4715–4733MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. Proceedings of the 24th International Conference on Artificial Intelligence. pp. 1617–1623. Buenos AiresGoogle Scholar
  21. 21.
    Lv J, Shao X, Shui J, Xiang D, Zhou H, Zhou X (2017) Data augmentation for face recognition. Neurocomputing. 230:184–196CrossRefGoogle Scholar
  22. 22.
    Martinel N, Piciarelli C, Foresti G, Micheloni C (2016) Mobile food recognition with an extreme deep tree. Proceedings of the 10th International Conference on Distributed Smart CameraGoogle Scholar
  23. 23.
    Marzorati M, Balloi A, De Ferra F, Corallo L, Carpani G, Wittebolle L, Verstraete W, Daffonchio D (2010) Bacterial diversity and reductive dehalogenase redundancy in a 1,2-dichloroethane-degrading bacterial consortium enriched from a contaminated aquifer. Microb Cell Factories 9:12CrossRefGoogle Scholar
  24. 24.
    Mohapatra S, Patra D, Satpathy S (2014) An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput & Applic 24:1887–1904CrossRefGoogle Scholar
  25. 25.
    Mousa F, El-Khoribi R, Shoman M (2016) A Novel Brain Computer Interface Based on Principle Component Analysis. Procedia Computer Science 82:49–56CrossRefGoogle Scholar
  26. 26.
    Neoh SC, Srisukkham W, Zhang L, Todryk S, Greystoke B, Lim CP, Hossain MA, Aslam N (2015) An intelligent decision support system for leukaemia diagnosis using microscopic blood images. Scientific Reports. Nat. Pub. Group 5 14938Google Scholar
  27. 27.
    Parvaresh H, Sajedi H, Rahimi SAH (2018) Leukemia Diagnosis using Image Processing and Computational Intelligence. 22nd IEEE International Conference on Intelligent Engineering Systems, Las PalmasCrossRefGoogle Scholar
  28. 28.
    Pérez-Llarena FJ, Bou G (2016) Proteomics As a Tool for Studying Bacterial Virulence and Antimicrobial Resistance. Front Microbiol 7:410CrossRefGoogle Scholar
  29. 29.
    Putman M, Burton R, Nahm NH (2005) Simplified method to automatically count bacterial colony forming unit. J Immunol Methods 302(1–2):99–102CrossRefGoogle Scholar
  30. 30.
    Rahimi SAH, Sajedi H, Mohammadipanah F (2017) Differentiation of identical Actinobacterial strains by Wavelet Transform and Artificial Neural Network. IEEE 15th International Symposium on Intelligent Systems and Informatics. SISY 2017Google Scholar
  31. 31.
    Razavi SF, Sajedi H, Shiri ME (2017) Integration of colour and uniform interlaced derivative patterns for object tracking. IET Image Process 10(5):381–390CrossRefGoogle Scholar
  32. 32.
    Rohania A, Takib M, Abdollahpour M (2018) A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renew Energy 115:411–422CrossRefGoogle Scholar
  33. 33.
    Sajedi H, Mohammadipanah F, Kazemi Shariat Panahi H (2018) An image analysis-aided redundancy reduction method for differentiation of identical Actinobacterial strains. Future Microbiol 13(3):313–329CrossRefGoogle Scholar
  34. 34.
    Salaken SM, Khosravi A, Nguyen T, Nahavandi S (2017) Extreme learning machine based transfer learning algorithms: A survey. Neuro Computing 267:516–524Google Scholar
  35. 35.
    Singh AK, Bettasso AM, Bae E, Rajwa B, Dundar MM, Forster MD, Liu L, Barrett B, Lovchik J, Robinson JP, Hirleman ED, Bhunia AK (2014) Laser optical sensor. a label-free on-plate Salmonella enterica colony detection tool. MBio. 5(1):e01019–e01013CrossRefGoogle Scholar
  36. 36.
    Srisukkham W, Zhang L, Neoh S, Todryk S, Lim C (2017) Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization. Appl Soft Comput 56:405–419CrossRefGoogle Scholar
  37. 37.
    VijayaLakshmi B, Mohan V (2016) Kernel-based PSO and FRVM: An automatic plant leaf type detection using texture, shape, and color features. Comput Electron Agric 125:99–112CrossRefGoogle Scholar
  38. 38.
    Weng Q, Mao Z, Lin J, Liao X (2018) Land-Use Scene Classification Based on a CNN Using a Constrained Extreme Learning Machine. Int J Remote Sens 39(19):6281–6299CrossRefGoogle Scholar
  39. 39.
    Yup Lee D, Bowen BP, Northen TR (2010) Mass spectrometry–based metabolomics, analysis of metabolite-protein interactions, and imaging. Biotechniques. 49(2):557–565CrossRefGoogle Scholar
  40. 40.
    Zhang S, Wu X, You Z, Zhang L (2017) Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric 134:135–141CrossRefGoogle Scholar
  41. 41.
    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):1–14. e0184554. Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of ScienceUniversity of TehranTehranIran
  2. 2.Pharmaceutical Biotechnology Lab, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of ScienceUniversity of TehranTehranIran

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