Actinobacterial strains recognition by Machine learning methods
- 58 Downloads
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.
KeywordsActinobacterial 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.
This article does not contain any studies with human participants performed by any of the authors.
- 3.Bahrami M, Sajedi H (2019) Image concept detection in imbalanced datasets with ensemble of convolutional neural networks. Intelligent Data Analysis. In PressGoogle Scholar
- 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
- 13.Kaiming H, Zhang X, Ren S (2015) Deep Residual Learning for Image Recognition, Microsoft Research, eprint arXiv:1512.03385
- 14.Kingma DP, Ba J, (2015) Adam: a method for stochastic optimization. 3rd International Conference for Learning Representations, San DiegoGoogle Scholar
- 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.LeCun Y, Kavukcuoglu K, Farabet C (2010) convolutional networks and applications in vision. International Symposium on Circuits and Systems (ISCAS)Google Scholar
- 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
- 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
- 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.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
- 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
- 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
- 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