Bacterial Classification Using Deep Structured Convolutional Neural Network for Low Resource Data
M Faizal Amri (1*), Asri Rizki Yuliani (2), Dwi Esti Kusumandari (3), Artha Ivonita Simbolon (4), M. Ilham Rizqyawan (5), Ulfah Nadiya (6) (1) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(2) Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency - Indonesia
(3) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(4) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(5) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(6) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(*) Corresponding Author
Received: January 19, 2023; Revised: March 31, 2023
Accepted: April 26, 2023; Published: August 31, 2023
Abstract
Bacterial identification is an essential task in medical disciplines and food hygiene. The characteristics of bacteria can be examined under a microscope using culture techniques. However, traditional clinical laboratory culture methods require considerable work, primarily physical and manual effort. An automated process using deep learning technology has been widely used for increasing accuracy and decreasing working costs. In this paper, our research evaluates different types of existing deep CNN models for bacterial contamination classification when low-resource data are used. They are baseline CNN, GCNN, ResNet, and VGGNet. The performance of CNN models was also compared with the traditional machine learning method, including SIFT+SVM. The performance of the DIBaS dataset and our own collected dataset have been evaluated. The results show that VGGNet achieves the highest accuracy. In addition, data augmentation was performed to inflate the dataset. After fitting the model with augmented data, the results show that the accuracy increases significantly. This improvement is consistent in all models and both datasets.
http://dx.doi.org/10.55981/jet.533 Keywords
References
S. Jamal, M. Khubaib, R. Gangwar, S. Grover, A. Grover, and S. E. Hasnain, “Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis,” Sci. Rep., vol. 10, no. 1, pp. 1–16, Mar. 2020, doi: 10.1038/s41598-020-62368-2. Crossref
A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, and J. J. P. C. Rodrigues, “Identifying pneumonia in chest X-rays: A deep learning approach,” Measurement, vol. 145, pp. 511–518, Oct. 2019, doi: 10.1016/J.MEASUREMENT.2019.05.076. Crossref
M. Deckers et al., “Strategy for the identification of micro-organisms producing food and feed products: Bacteria producing food enzymes as study case,” Food Chem., vol. 305, Feb. 2020, doi: 10.1016/J.FOODCHEM.2019.125431. Crossref
S. A. Nehal, D. Roy, M. Devi, and T. Srinivas, “Highly sensitive lab-on-chip with deep learning AI for detection of bacteria in water,” International Journal of Information Technology (Singapore), vol. 12, no. 2, pp. 495–501, Jun. 2020, doi: 10.1007/S41870-019-00363-1/METRICS. Crossref
F. M. Khan, R. Gupta, and S. Sekhri, “Automated Bacteria Colony Counting on Agar Plates Using Machine Learning,” J. Inf. Technol., vol. 147, no. 12, p. 04021066, Oct. 2021, doi: 10.1061/(ASCE)EE.1943-7870.0001948. Crossref
X. Liu et al., “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,” Lancet Digit. Health, vol. 1, no. 6, pp. e271–e297, Oct. 2019, doi: 10.1016/S2589-7500(19)30123-2. Crossref
A. B. Levine, C. Schlosser, J. Grewal, R. Coope, S. J. M. Jones, and S. Yip, “Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis,” Trends Cancer, vol. 5, no. 3, pp. 157–169, Mar. 2019, doi: 10.1016/J.TRECAN.2019.02.002. Crossref
P. Rani, S. Kotwal, J. Manhas, V. Sharma, and S. Sharma, “Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments,” Arch. Comput. Methods Eng., vol. 29, no. 3, pp. 1801–1837, Aug. 2021, doi: 10.1007/S11831-021-09639-X. Crossref
J. L. Deglint, C. Jin, A. Chao, and A. Wong, “The Feasibility of Automated Identification of Six Algae Types Using Feed-Forward Neural Networks and Fluorescence-Based Spectral-Morphological Features,” IEEE Access, vol. 7, pp. 7041–7053, 2019, doi: 10.1109/ACCESS.2018.2889017. Crossref
M. A. E. Abdalla and H. Seker, “Recognition of protozoan parasites from microscopic images: Eimeria species in chickens and rabbits as a case study,” in Conf Proc. IEEE Eng. Med. Biol. Soc. (EMBS), Jul. 2017, pp. 1517–1520, doi: 10.1109/EMBC.2017.8037124. Crossref
B. Zieliski, A. Sroka-Oleksiak, D. Rymarczyk, A. Piekarczyk, and M. Brzychczy-Woch, “Deep learning approach to describe and classify fungi microscopic images,” PLoS One, vol. 15, no. 6, pp. 1-16, Jun. 2020, doi: 10.1371/JOURNAL.PONE.0234806. Crossref
H. Ma et al., “Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy,” J. Microbiol., vol. 59, no. 6, pp. 563–572, Jun. 2021, doi: 10.1007/S12275-021-1013-Z/METRICS. Crossref
J. Zhang et al., “A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches,” Artif. Intell. Rev., vol. 55, no. 4, pp. 2875–2944, Apr. 2022, doi: 10.1007/S10462-021-10082-4/FIGURES/55. Crossref
P. Hiremath and P. Bannigidad, “Automated gram-staining characterization of digital bacterial cell images,” in Proc. IEEE Int’l. Conf. on Signal and Image Processing ICSIP, no. August 2009, 2009.
P. S. Hiremath and P. Bannigidad, “Identification and classification of cocci bacterial cells in digital microscopic images,” Int. J. Comput. Biol. Drug. Des., vol. 4, no. 3, pp. 262–273, 2011, doi: 10.1504/IJCBDD.2011.041414. Crossref
P. Ghosh, D. Bhattacharjee, and M. Nasipuri, “A hybrid approach to diagnosis of tuberculosis from sputum,” in Int. Conf. Electr. Eng. Inform. Commun. Technol. (ICEEOT) 2016, pp. 771–776, Nov. 2016, doi: 10.1109/ICEEOT.2016.7754790. Crossref
H. Wang et al., “Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning,” Light Sci. Appl, vol. 9, no. 1, pp. 1–17, Jul. 2020, doi: 10.1038/s41377-020-00358-9. Crossref
A. Ferrari, S. Lombardi, and A. Signoroni, “Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging,” Pattern Recognit., vol. 61, pp. 629–640, Jan. 2017, doi: 10.1016/J.PATCOG.2016.07.016. Crossref
E. A. Hay and R. Parthasarathy, “Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets,” PLoS Comput. Biol., vol. 14, no. 12, p. e1006628, Dec. 2018, doi: 10.1371/JOURNAL.PCBI.1006628. Crossref
M. K. Osman, M. Y. Mashor, and H. Jaafar, “Hybrid multilayered perceptron network trained by modified recursive prediction error-extreme learning machine for tuberculosis bacilli detection,” in 5th Kuala Lumpur International Conference on Biomedical Engineering 2011, IFMBE Proc., vol. 35, Springer, Berlin, Heidelberg, 2011, pp. 667–673, doi: 10.1007/978-3-642-21729-6_163/COVER. Crossref
M. G. Forero, G. Cristóbal, and M. Desco, “Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models,” J. Microsc., vol. 223, no. 2, pp. 120–132, Aug. 2006, doi: 10.1111/J.1365-2818.2006.01610.X. Crossref
M. F. Wahid, T. Ahmed, and M. A. Habib, “Classification of microscopic images of bacteria using deep convolutional neural network,” in The 10th Int. Conf. Electr. Eng. Inform. Commun. Technol. (ICECE) 2018, pp. 217–220, Feb. 2019, doi: 10.1109/ICECE.2018.8636750. Crossref
B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Włoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One, vol. 12, no. 9, p. e0184554, Sep. 2017, doi: 10.1371/JOURNAL.PONE.0184554. Crossref
Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language Modeling with Gated Convolutional Networks,” in Proceedings of the 34th International Conference on Machine Learning, Aug. 2017, pp. 933-941, doi: 10.5555/3305381.3305478. Crossref
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press. [Online]: https://www.scirp.org/(S(czeh2tfqw2orz553k1w0r45))/reference/referencespapers.aspx?referenceid=2859809(accessed Jan. 14, 2023).
Y. Bengio, Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, vol. 2, no. 1, pp. 1–27, Jan. 2009, doi: 10.1561/2200000006. Crossref
P. Lu, X. J. Peng, J. B. Yu, and X. Peng, “Gated CNN for visual quality assessment based on color perception,” Signal Process. Image Commun., vol. 72, pp. 105–112, Mar. 2019, doi: 10.1016/J.IMAGE.2018.12.007. Crossref
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 770–778, Jun. 2016, doi: 10.1109/CVPR.2016.90. Crossref
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in 3rd International Conference on Learning Representations (ICLR) 2015, Conference Track Proceedings, May 2015, doi: 10.48550/arxiv.1409.1556. Crossref
Article Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 National Research and Innovation Agency
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Email this article 
