Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements
DOI:
https://doi.org/10.4108/eetiot.4484Keywords:
Microorganisms, biotechnology, classification, clinical microbiology, food production, Amoeba, Euglena, Hydra, Paramecium, Rod bacteria, Spherical bacteria, Spiral bacteria, Yeast, SVM, Random Forest, KNN, CNNAbstract
Microorganisms are pervasive and have a significant impact in various fields such as healthcare, environmental monitoring, and biotechnology. Accurate classification and identification of microorganisms are crucial for professionals in diverse areas, including clinical microbiology, agriculture, and food production. Traditional methods for analyzing microorganisms, like culture techniques and manual microscopy, can be labor-intensive, expensive, and occasionally inadequate due to morphological similarities between different species. As a result, there is an increasing need for intelligent image recognition systems to automate microorganism classification procedures with minimal human involvement. In this paper, we present an in-depth analysis of ML and DL perspectives used for the precise recognition and classification of microorganism images, utilizing a dataset comprising eight distinct microorganism types: Spherical bacteria, Amoeba, Hydra, Paramecium, Rod bacteria, Spiral bacteria, Euglena and Yeast. We employed several ml algorithms including SVM, Random Forest, and KNN, as well as the deep learning algorithm CNN. Among these methods, the highest accuracy was achieved using the CNN approach. We delve into current techniques, challenges, and advancements, highlighting opportunities for further progress.
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Environmental Research; Study Results from D.H. Ma and Colleagues Broaden Understanding of Environmental Research (Biological removal of antiandrogenic activity in gray wastewater and coking wastewater by membrane reactor process) (p. 880). (2015). NewsRx.
Lin, F., Duan, Q.-Y., & Wu, F.-G. (2020). Conjugated Polymer-Based Photothermal Therapy for Killing Microorganisms. ACS Applied Polymer Materials, 2(10), 4331–4344. https://doi.org/10.1021/acsapm.0c00718 DOI: https://doi.org/10.1021/acsapm.0c00718
Gracias, K. S., & McKillip, J. L. (2011). Triplex PCR-based detection of enterotoxigenic Bacillus cereus ATCC 14579 in nonfat dry milk. Journal of Basic Microbiology, 51(2), 147–152. https://doi.org/10.1002/jobm.200900348 DOI: https://doi.org/10.1002/jobm.200900348
Daims, H., & Wagner, M. (2018). Nitrospira. Trends in Microbiology (Regular Ed.), 26(5), 462–463. https://doi.org/10.1016/j.tim.2018.02.001 DOI: https://doi.org/10.1016/j.tim.2018.02.001
Barbedo, J. G. A. (2012). Method for Counting Microorganisms and Colonies in Microscopic Images. 2012 12th International Conference on Computational Science and Its Applications, 83–87. https://doi.org/10.1109/ICCSA.2012.23 DOI: https://doi.org/10.1109/ICCSA.2012.23
Dazzo, F., Sexton, R., Jain, A., Makhoul, A., Shears, M., Gusfa, D., Handelsman, S., Niccum, B., & Onsay, D. (2017). Influence of Substratum Hydrophobicity on the Geomicrobiology of River Biofilm Architecture and Ecology Analyzed by CMEIAS Bioimage Informatics. Geosciences (Basel), 7(3), 56. https://doi.org/10.3390/geosciences7030056 DOI: https://doi.org/10.3390/geosciences7030056
Antharam, V. C., McEwen, D. C., Garrett, T. J., Dossey, A. T., Li, E. C., Kozlov, A. N., Mesbah, Z., & Wang, G. P. (2016). An Integrated Metabolomic and Microbiome Analysis Identified Specific Gut Microbiota Associated with Fecal Cholesterol and Coprostanol in Clostridium difficile Infection. PloS One, 11(2), e0148824–e0148824. https://doi.org/10.1371/journal.pone.0148824 DOI: https://doi.org/10.1371/journal.pone.0148824
Puchkov, E. O. (2019). Quantitative Methods for Single-Cell Analysis of Microorganisms. Microbiology (New York), 88(1), 1–14. https://doi.org/10.1134/S0026261719010120 DOI: https://doi.org/10.1134/S0026261719010120
Shi, L., Dong, H., Reguera, G., Beyenal, H., Lu, A., Liu, J., Yu, H.-Q., & Fredrickson, J. K. (2016). Extracellular electron transfer mechanisms between microorganisms and minerals. Nature Reviews. Microbiology, 14(10), 651–662. https://doi.org/10.1038/nrmicro.2016.93 DOI: https://doi.org/10.1038/nrmicro.2016.93
Fan, W., Huang, X., Liu, K., Xu, Y., & Chi, Z. (2023). Advanced upcycling of agro-industrial co-products of corn via different microorganisms. Biomass & Bioenergy, 168, 106669. https://doi.org/10.1016/j.biombioe.2022.106669 DOI: https://doi.org/10.1016/j.biombioe.2022.106669
Rani, P., Kotwal, S., Manhas, J., Sharma, V., & Sharma, S. (2022). Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Archives of Computational Methods in Engineering, 29(3), 1801–1837. https://doi.org/10.1007/s11831-021-09639-x DOI: https://doi.org/10.1007/s11831-021-09639-x
Zhang, Y., Jiang, H., Ye, T., & Juhas, M. (2021). Deep Learning for Imaging and Detection of Microorganisms. Trends in Microbiology (Regular Ed.), 29(7), 569–572. https://doi.org/10.1016/j.tim.2021.01.006 DOI: https://doi.org/10.1016/j.tim.2021.01.006
Liang, C.-M., Lai, C.-C., Wang, S.-H., & Lin, Y.-H. (2021). Environmental microorganism classification using optimized deep learning model. Environmental Science and Pollution Research International, 28(24), 31920–31932. https://doi.org/10.1007/s11356-021-13010-9 DOI: https://doi.org/10.1007/s11356-021-13010-9
Kulwa, F., Li, C., Zhang, J., Shirahama, K., Kosov, S., Zhao, X., Jiang, T., & Grzegorzek, M. (2022). A new pairwise deep learning feature for environmental microorganism image analysis. Environmental Science and Pollution Research International, 29(34), 51909–51926. https://doi.org/10.1007/s11356-022-18849-0 DOI: https://doi.org/10.1007/s11356-022-18849-0
Yang, H., Zhao, X., Jiang, T., Zhang, J., Zhao, P., Chen, A., Grzegorzek, M., Qi, S., Teng, Y., & Li, C. (2022). Comparative Study for Patch-Level and Pixel-Level Segmentation of Deep Learning Methods on Transparent Images of Environmental Microorganisms: From Convolutional Neural Networks to Visual Transformers. Applied Sciences, 12(18), 9321. https://doi.org/10.3390/app12189321 DOI: https://doi.org/10.3390/app12189321
Zhang, J., Li, C., Rahaman, M. M., Yao, Y., Ma, P., Zhang, J., Zhao, X., Jiang, T., & Grzegorzek, M. (2022). A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. The Artificial Intelligence Review, 55(4), 2875–2944. https://doi.org/10.1007/s10462-021-10082-4 DOI: https://doi.org/10.1007/s10462-021-10082-4
Ma, P., Li, C., Rahaman, M. M., Yao, Y., Zhang, J., Zou, S., Zhao, X., & Grzegorzek, M. (2023). A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. The Artificial Intelligence Review, 56(2), 1627–1698. https://doi.org/10.1007/s10462-022-10209-1 DOI: https://doi.org/10.1007/s10462-022-10209-1
Biassoni, R., Di Marco, E., Squillario, M., Barla, A., Piccolo, G., Ugolotti, E., Gatti, C., Minuto, N., Patti, G., Maghnie, M., & d'Annunzio, G. (2020). Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked. The Journal of Clinical Endocrinology and Metabolism, 105(9), E3114–E3126. https://doi.org/10.1210/clinem/dgaa407 DOI: https://doi.org/10.1210/clinem/dgaa407
Nocedo-Mena, D., Cornelio, C., Camacho-Corona, M. del R., Garza-Gonzalez, E., Waksman de Torres, N., Arrasate, S., Sotomayor, N., Lete, E., & Gonzalez-Diaz, H. (2019). Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks. Journal of Chemical Information and Modeling, 59(3), 1109–1120. https://doi.org/10.1021/acs.jcim.9b00034 DOI: https://doi.org/10.1021/acs.jcim.9b00034
Pei, Z., Liu, S., Jing, Z., Zhang, Y., Wang, J., Liu, J., Wang, Y., Guo, W., Li, Y., Feng, L., Zhou, H., Li, G., Han, Y., Liu, D., & Pan, J. (2022). Understanding of the interrelationship between methane production and microorganisms in high-solid anaerobic co-digestion using microbial analysis and machine learning. Journal of Cleaner Production, 373, 133848. https://doi.org/10.1016/j.jclepro.2022.133848 DOI: https://doi.org/10.1016/j.jclepro.2022.133848
Huang, T.-S., Lee, S. S.-J., Lee, C.-C., & Chang, F.-C. (2020). Detection of carbapenem-resistant Klebsiella pneumoniae on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using supervised machine learning approach. PloS One, 15(2), e0228459–e0228459. https://doi.org/10.1371/journal.pone.0228459 DOI: https://doi.org/10.1371/journal.pone.0228459
Nakanishi, A., Fukunishi, H., Matsumoto, R., & Eguchi, F. (2022). Development of a Prediction Method of Cell Density in Autotrophic/Heterotrophic Microorganism Mixtures by Machine Learning Using Absorbance Spectrum Data. Biotech (Basel), 11(4), 46. https://doi.org/10.3390/biotech11040046 DOI: https://doi.org/10.3390/biotech11040046
Yu, T., Su, S., Hu, J., Zhang, J., & Xianyu, Y. (2022). A New Strategy for Microbial Taxonomic Identification through Micro‐Biosynthetic Gold Nanoparticles and Machine Learning. Advanced Materials (Weinheim), 34(11), e2109365–n/a. https://doi.org/10.1002/adma.202109365 DOI: https://doi.org/10.1002/adma.202109365
Maeda, Y., Kohketsu, H., Tanaka, Y., Sugiyama, Y., Kogiso, A., Lim, T.-K., Harada, M., Yoshino, T., Matsunaga, T., & Tanaka, T. (2020). (Invited) Rapid and Robust Discrimination of Food-Contaminating Microorganisms Guided By Machine Learning. Meeting Abstracts (Electrochemical Society), MA2020-02(44), 2812–2812. https://doi.org/10.1149/MA2020-02442812mtgabs DOI: https://doi.org/10.1149/MA2020-02442812mtgabs
Zhang, J., & Fernando, S. D. (2023). Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning. Microorganisms (Basel), 11(5), 1341. https://doi.org/10.3390/microorganisms11051341 DOI: https://doi.org/10.3390/microorganisms11051341
Gado, J. E., Beckham, G. T., & Payne, C. M. (2020). Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning. Journal of Chemical Information and Modeling, 60(8), 4098–4107. https://doi.org/10.1021/acs.jcim.0c00489 DOI: https://doi.org/10.1021/acs.jcim.0c00489
Truong, V. K., Chapman, J., & Cozzolino, D. (2021). Monitoring the Bacterial Response to Antibiotic and Time Growth Using Near-infrared Spectroscopy Combined with Machine Learning. Food Analytical Methods, 14(7), 1394–1401. https://doi.org/10.1007/s12161-021-01994-6 DOI: https://doi.org/10.1007/s12161-021-01994-6
Bemani, A., Kazemi, A., & Ahmadi, M. (2023). An insight into the microorganism growth prediction by means of machine learning approaches. Journal of Petroleum Science & Engineering, 220, 111162. https://doi.org/10.1016/j.petrol.2022.111162 DOI: https://doi.org/10.1016/j.petrol.2022.111162
Abdullah, A. A., Aziz, A. N. A., Kanaya, S., & Ranjan Dash, S. (2019). Classification of Microorganism Species Based on Volatile Metabolite Contents Similarity. Journal of Physics. Conference Series, 1372(1), 12061. https://doi.org/10.1088/1742-6596/1372/1/012061 DOI: https://doi.org/10.1088/1742-6596/1372/1/012061
Helena Tavares Kennedy. (2021). Biofuels Digest: Pollution pods, machine learning, fermentation protein-producing microorganisms, Microsoft’s look at algae and fungi for green data centers and more: The Digest’s Top 8 Innovations for the week of November 4th. In Newstex Trade & Industry Blogs. Newstex.
Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images, Sara A Althubiti, Sanchita Paul, Rajnikanta Mohanty, Sachi Nandan Mohanty, Fayadh Alenezi, Kemal Polat, Computational and Mathematical Methods in Medicine (Hindawi), 2022, doi.org/10.1155/2022/2733965 DOI: https://doi.org/10.1155/2022/2733965
A Novel Approach for Diabetic Retinopathy Screening Using Asymmetric Deep Learning Feature, Pradeep Kumar Jena, Bonomali Khuntia, Charulata Palai, Manjushree Nayak, Tapas Kumar Mishra, Sachi Nandan Mohanty, Big Data Cognitive Computing (2023), Vol 7, Issue 1, 25, https://doi.org/10.3390/bdcc7010025, ISSN: 2504-2289 DOI: https://doi.org/10.3390/bdcc7010025
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