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, 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
Article
  • 58 Downloads

Abstract

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.

Keywords

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

Notes

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.

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Copyright information

© 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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