Research Article
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Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater

Year 2022, Volume: 5 Issue: 1, 101 - 110, 31.03.2022
https://doi.org/10.35208/ert.969400

Abstract

Identifying the microbial population and type of them is a crucial measure in the water and wastewater treatment processes, reuse of wastewater, and sludge treatment system. Todays, manual methods are usually used to count and detect the type of bacteria in water and sewage laboratories which mostly suffer from human errors. This study aims at presenting an accurate method based on image analysis through the convolution neural network (CNN) to classify Escherichia coli (E. coli) and Vibrio cholera (V. cholera) bacteria, in wastewater. About 9,000 Red-Green-Blue (RGB) microscopic images of the sewage sample containing the stained bacteria were used as the input datasets. The results showed that the bacteria would be classified and counted with the accuracy of 93.01% and 97.0%, respectively. While CNN performed pretty well in counting the number of bacteria for both RGB and grayscale color models, its classification performance is only satisfactory in the RGB images. The sensitivity analysis of CNN illustrated that the Gaussian noise enhancement caused to the increment in the standard deviation () that proportionally decreased the CNN accuracy.

References

  • • Abtahi, S., Seyed Sharifi, R. and Qaderi, F. (2014) Influence of nitrogen fertilizer rates and seed inoculation with plant growth promoting rhizobacteria (PGPR) on yield, fertilizer use efficiency, rate and effective grain filling period of soybean (Glycine max L.) in second cropping. Journal of Agricultural Science and Sustainable Production 24(3), 112-129.
  • • Akbarian Mymand, M.j., farji kafshgari, s., sadeghi mahounak, a., hoseyni sharghi, s.a. and vatan khah, m. (2014) Investigate the feasibility of using image processing method for the count of bacteria and comparison with Colony Counter. Iranian Journal of Medical Microbiology 8(2), 8-13.
  • • Asadi, P., Rad, H.A. and Qaderi, F. (2019) Comparison of Chlorella vulgaris and Chlorella sorokiniana pa. 91 in post treatment of dairy wastewater treatment plant effluents. Environmental Science and Pollution Research 26(28), 29473-29489.
  • • Azis, F.A., Suhaimi, H. and Abas, E. (2020) Waste Classification using Convolutional Neural Network, pp. 9-13.
  • • Bahrani, A., Majidi, B. and Eshghi, M. (2019) Coral Reef Management in Persian Gulf Using Deep Convolutional Neural Networks, pp. 200-204, IEEE.
  • • Bitton, G. (2005) Wastewater microbiology, John Wiley & Sons.
  • • Edition, F. (2011) Guidelines for drinking-water quality. WHO chronicle 38(4), 104-108.
  • • Fujioka, R.S., Solo-Gabriele, H.M., Byappanahalli, M.N. and Kirs, M. (2015) US recreational water quality criteria: a vision for the future. International journal of environmental research and public health 12(7), 7752-7776.
  • • Gupta, A. and Ruebush, E. (2019) Aquasight: Automatic water impurity detection utilizing convolutional neural networks. arXiv preprint arXiv:1907.07573.
  • • Hartigan, J.A. and Wong, M.A. (1979) AK‐means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics) 28(1), 100-108.
  • • Houpikian, P. and Raoult, D. (2002) Traditional and molecular techniques for the study of emerging bacterial diseases: one laboratory’s perspective. Emerging infectious diseases 8(2), 122.
  • • Huang, L. and Wu, T. (2018) Novel neural network application for bacterial colony classification. Theoretical Biology and Medical Modelling 15(1), 1-16.
  • • Jarvis, B. (2016) Statistical Aspects of the Microbiological Examination of Foods (Third Edition), pp. 119-140, Academic Press.
  • • Jarvis, B., Hedges, A.J. and Corry, J.E. (2012) The contribution of sampling uncertainty to total measurement uncertainty in the enumeration of microorganisms in foods. Food microbiology 30(2), 362-371.
  • • Kalajdjieski, J., Zdravevski, E., Corizzo, R., Lameski, P., Kalajdziski, S., Pires, I.M., Garcia, N.M. and Trajkovik, V. (2020) Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks. Remote Sensing 12(24), 4142.
  • • LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. nature 521(7553), 436-444.
  • • Macawile, M.J., Quiñones, V.V., Ballado, A., Cruz, J.D. and Caya, M.V. (2018) White blood cell classification and counting using convolutional neural network, pp. 259-263, IEEE.
  • • Medema, G. (2003) Assessing Microbial Safety of Drinking Water, IWA. • Naidoo, S. and Olaniran, A.O. (2014) Treated wastewater effluent as a source of microbial pollution of surface water resources. International journal of environmental research and public health 11(1), 249-270.
  • • NIK, B. (2005) Intensity Transformations and Spatial Filtering.
  • • Odonkor, S.T. and Ampofo, J.K. (2013) Escherichia coli as an indicator of bacteriological quality of water: an overview. Microbiology research 4(1), 5-11.
  • • Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P. and Zheng, Y. (2016) Convolutional neural networks for diabetic retinopathy. Procedia computer science 90, 200-205.
  • • Qaderi, F., Ayati, B. and Ganjidoust, H. (2011) Role of moving bed biofilm reactor and sequencing batch reactor in biological degradation of formaldehyde wastewater.
  • • Qaderi, F. and Babanezhad, E. (2017) Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network. Journal of Cleaner Production 161, 840-849.
  • • Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A. and Jung, J. (2020) Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. Neural Networks 121, 396-408.
  • • Shaily, T. and Kala, S. (2020) Bacterial Image Classification Using Convolutional Neural Networks, pp. 1-6, IEEE.
  • • Sun, L., Yan, H., Xin, K. and Tao, T. (2019) Contamination source identification in water distribution networks using convolutional neural network. Environmental Science and Pollution Research 26(36), 36786-36797.
  • • Talo, M. (2019) An automated deep learning approach for bacterial image classification. arXiv preprint arXiv:1912.08765.
  • • Tamiev, D., Furman, P.E. and Reuel, N.F. (2020) Automated classification of bacterial cell sub-populations with convolutional neural networks. PloS one 15(10), e0241200.
  • • Yurtsever, M. and Yurtsever, U. (2019) Use of a convolutional neural network for the classification of microbeads in urban wastewater. Chemosphere 216, 271-280.
  • • Zhang, Y.J. (1996) A survey on evaluation methods for image segmentation. Pattern recognition 29(8), 1335-1346.
Year 2022, Volume: 5 Issue: 1, 101 - 110, 31.03.2022
https://doi.org/10.35208/ert.969400

Abstract

References

  • • Abtahi, S., Seyed Sharifi, R. and Qaderi, F. (2014) Influence of nitrogen fertilizer rates and seed inoculation with plant growth promoting rhizobacteria (PGPR) on yield, fertilizer use efficiency, rate and effective grain filling period of soybean (Glycine max L.) in second cropping. Journal of Agricultural Science and Sustainable Production 24(3), 112-129.
  • • Akbarian Mymand, M.j., farji kafshgari, s., sadeghi mahounak, a., hoseyni sharghi, s.a. and vatan khah, m. (2014) Investigate the feasibility of using image processing method for the count of bacteria and comparison with Colony Counter. Iranian Journal of Medical Microbiology 8(2), 8-13.
  • • Asadi, P., Rad, H.A. and Qaderi, F. (2019) Comparison of Chlorella vulgaris and Chlorella sorokiniana pa. 91 in post treatment of dairy wastewater treatment plant effluents. Environmental Science and Pollution Research 26(28), 29473-29489.
  • • Azis, F.A., Suhaimi, H. and Abas, E. (2020) Waste Classification using Convolutional Neural Network, pp. 9-13.
  • • Bahrani, A., Majidi, B. and Eshghi, M. (2019) Coral Reef Management in Persian Gulf Using Deep Convolutional Neural Networks, pp. 200-204, IEEE.
  • • Bitton, G. (2005) Wastewater microbiology, John Wiley & Sons.
  • • Edition, F. (2011) Guidelines for drinking-water quality. WHO chronicle 38(4), 104-108.
  • • Fujioka, R.S., Solo-Gabriele, H.M., Byappanahalli, M.N. and Kirs, M. (2015) US recreational water quality criteria: a vision for the future. International journal of environmental research and public health 12(7), 7752-7776.
  • • Gupta, A. and Ruebush, E. (2019) Aquasight: Automatic water impurity detection utilizing convolutional neural networks. arXiv preprint arXiv:1907.07573.
  • • Hartigan, J.A. and Wong, M.A. (1979) AK‐means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics) 28(1), 100-108.
  • • Houpikian, P. and Raoult, D. (2002) Traditional and molecular techniques for the study of emerging bacterial diseases: one laboratory’s perspective. Emerging infectious diseases 8(2), 122.
  • • Huang, L. and Wu, T. (2018) Novel neural network application for bacterial colony classification. Theoretical Biology and Medical Modelling 15(1), 1-16.
  • • Jarvis, B. (2016) Statistical Aspects of the Microbiological Examination of Foods (Third Edition), pp. 119-140, Academic Press.
  • • Jarvis, B., Hedges, A.J. and Corry, J.E. (2012) The contribution of sampling uncertainty to total measurement uncertainty in the enumeration of microorganisms in foods. Food microbiology 30(2), 362-371.
  • • Kalajdjieski, J., Zdravevski, E., Corizzo, R., Lameski, P., Kalajdziski, S., Pires, I.M., Garcia, N.M. and Trajkovik, V. (2020) Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks. Remote Sensing 12(24), 4142.
  • • LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. nature 521(7553), 436-444.
  • • Macawile, M.J., Quiñones, V.V., Ballado, A., Cruz, J.D. and Caya, M.V. (2018) White blood cell classification and counting using convolutional neural network, pp. 259-263, IEEE.
  • • Medema, G. (2003) Assessing Microbial Safety of Drinking Water, IWA. • Naidoo, S. and Olaniran, A.O. (2014) Treated wastewater effluent as a source of microbial pollution of surface water resources. International journal of environmental research and public health 11(1), 249-270.
  • • NIK, B. (2005) Intensity Transformations and Spatial Filtering.
  • • Odonkor, S.T. and Ampofo, J.K. (2013) Escherichia coli as an indicator of bacteriological quality of water: an overview. Microbiology research 4(1), 5-11.
  • • Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P. and Zheng, Y. (2016) Convolutional neural networks for diabetic retinopathy. Procedia computer science 90, 200-205.
  • • Qaderi, F., Ayati, B. and Ganjidoust, H. (2011) Role of moving bed biofilm reactor and sequencing batch reactor in biological degradation of formaldehyde wastewater.
  • • Qaderi, F. and Babanezhad, E. (2017) Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network. Journal of Cleaner Production 161, 840-849.
  • • Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A. and Jung, J. (2020) Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. Neural Networks 121, 396-408.
  • • Shaily, T. and Kala, S. (2020) Bacterial Image Classification Using Convolutional Neural Networks, pp. 1-6, IEEE.
  • • Sun, L., Yan, H., Xin, K. and Tao, T. (2019) Contamination source identification in water distribution networks using convolutional neural network. Environmental Science and Pollution Research 26(36), 36786-36797.
  • • Talo, M. (2019) An automated deep learning approach for bacterial image classification. arXiv preprint arXiv:1912.08765.
  • • Tamiev, D., Furman, P.E. and Reuel, N.F. (2020) Automated classification of bacterial cell sub-populations with convolutional neural networks. PloS one 15(10), e0241200.
  • • Yurtsever, M. and Yurtsever, U. (2019) Use of a convolutional neural network for the classification of microbeads in urban wastewater. Chemosphere 216, 271-280.
  • • Zhang, Y.J. (1996) A survey on evaluation methods for image segmentation. Pattern recognition 29(8), 1335-1346.
There are 30 citations in total.

Details

Primary Language English
Subjects Environmental Engineering
Journal Section Research Articles
Authors

Tohid Irani 0000-0003-2736-7219

Hamid Amiri 0000-0001-9169-9619

Sama Azadi 0000-0002-1040-4645

Mohsen Bayat 0000-0002-8719-1975

Hedieh Deyhim 0000-0002-4077-3572

Publication Date March 31, 2022
Submission Date July 11, 2021
Acceptance Date January 31, 2022
Published in Issue Year 2022 Volume: 5 Issue: 1

Cite

APA Irani, T., Amiri, H., Azadi, S., Bayat, M., et al. (2022). Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater. Environmental Research and Technology, 5(1), 101-110. https://doi.org/10.35208/ert.969400
AMA Irani T, Amiri H, Azadi S, Bayat M, Deyhim H. Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater. ERT. March 2022;5(1):101-110. doi:10.35208/ert.969400
Chicago Irani, Tohid, Hamid Amiri, Sama Azadi, Mohsen Bayat, and Hedieh Deyhim. “Use of a Convolution Neural Network for the Classification of E. Coli and V. Cholara Bacteria in Wastewater”. Environmental Research and Technology 5, no. 1 (March 2022): 101-10. https://doi.org/10.35208/ert.969400.
EndNote Irani T, Amiri H, Azadi S, Bayat M, Deyhim H (March 1, 2022) Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater. Environmental Research and Technology 5 1 101–110.
IEEE T. Irani, H. Amiri, S. Azadi, M. Bayat, and H. Deyhim, “Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater”, ERT, vol. 5, no. 1, pp. 101–110, 2022, doi: 10.35208/ert.969400.
ISNAD Irani, Tohid et al. “Use of a Convolution Neural Network for the Classification of E. Coli and V. Cholara Bacteria in Wastewater”. Environmental Research and Technology 5/1 (March 2022), 101-110. https://doi.org/10.35208/ert.969400.
JAMA Irani T, Amiri H, Azadi S, Bayat M, Deyhim H. Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater. ERT. 2022;5:101–110.
MLA Irani, Tohid et al. “Use of a Convolution Neural Network for the Classification of E. Coli and V. Cholara Bacteria in Wastewater”. Environmental Research and Technology, vol. 5, no. 1, 2022, pp. 101-10, doi:10.35208/ert.969400.
Vancouver Irani T, Amiri H, Azadi S, Bayat M, Deyhim H. Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater. ERT. 2022;5(1):101-10.