{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T07:22:15Z","timestamp":1785223335474,"version":"3.55.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices.<\/jats:p>","DOI":"10.3389\/frai.2023.1200994","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:47:19Z","timestamp":1697701639000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Machine learning algorithms in microbial classification: a comparative analysis"],"prefix":"10.3389","volume":"6","author":[{"given":"Yuandi","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. Andrew","family":"Gadsden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1109\/ISRITI54043.2021.9702809","article-title":"\u201cAn enhanced classification of bacteria pathogen on microscopy images using deep learning,\u201d","volume-title":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","author":"Akbar","year":"2021"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24955-6_66","article-title":"\u201cWirtinger calculus based gradient descent and Levenberg-Marquardt learning algorithms in complex-valued neural networks,\u201d","author":"Amin","year":"2011","journal-title":"International Conference on Neural Information Processing"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.1128\/JCM.02267-07","article-title":"Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients","volume":"46","author":"Bosch","year":"2008","journal-title":"J. Clin. Microbiol."},{"key":"B4","first-page":"121","article-title":"\u201cThe influence of pattern similarity and transfer learning upon training of a base perceptron b2,\u201d","volume":"3","author":"Bozinovski","year":"1976","journal-title":"In Proceedings of Symposium Informatica"},{"key":"B5","volume-title":"Artificial immune recognition system (airs)-a review and analysis, Victoria: Center for Intelligent Systems and Complex Processes (CISCP), Faculty of Information and Communication Technologies","author":"Brownlee","year":"2005"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00881-5","article-title":"A transfer learning-based approach with deep cnn for COVID-19-and pneumonia-affected chest x-ray image classification","volume":"3","author":"Chakraborty","year":"2022","journal-title":"SN Comp. Sci."},{"key":"B7","first-page":"02941","article-title":"On the convergence of a class of adam-type algorithms for non-convex optimization","volume":"1808","author":"Chen","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"B8","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.bj.2017.09.001","article-title":"Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: a proof of concept","volume":"40","author":"Croxatto","year":"2017","journal-title":"Biomed. J."},{"key":"B9","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2009.5206848","article-title":"\u201cImagenet: a large-scale hierarchical image database,\u201d","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","author":"Deng","year":"2009"},{"key":"B10","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1134\/S1054661816010065","article-title":"A survey of deep learning methods and software tools for image classification and object detection","volume":"26","author":"Druzhkov","year":"2016","journal-title":"Patt. Recogn. Image Anal."},{"key":"B11","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"B12","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s10916-008-9241-x","article-title":"Tuberculosis disease diagnosis using artificial neural networks","volume":"34","author":"Er","year":"2010","journal-title":"J. Med. Syst."},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1109\/UBMK.2017.8093521","article-title":"\u201cData classification with deep learning using Tensorflow,\u201d","author":"Ertam","year":"2017","journal-title":"2017 International Conference on Computer Science and Engineering (UBMK)"},{"key":"B14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"B15","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1128\/JCM.01591-05","article-title":"Prospects for clinical application of electronic-nose technology to early detection of Mycobacterium tuberculosis in culture and sputum","volume":"44","author":"Fend","year":"2006","journal-title":"J. Clin. Microbiol."},{"key":"B16","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.patcog.2016.07.016","article-title":"Bacterial colony counting with convolutional neural networks in digital microbiology imaging","volume":"61","author":"Ferrari","year":"2017","journal-title":"Patt. Recognit."},{"key":"B17","doi-asserted-by":"publisher","first-page":"39915","DOI":"10.48550\/ARXIV.2106.06505","article-title":"Efficient deep learning architectures for fast identification of bacterial strains in resource-constrained devices","volume":"81","author":"Garc\u00eda","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"B18","doi-asserted-by":"publisher","first-page":"645972","DOI":"10.3389\/fmicb.2021.645972","article-title":"Efficient detection of longitudinal bacteria fission using transfer learning in deep neural networks","volume":"12","author":"Garcia-Perez","year":"2021","journal-title":"Front. Microbiol."},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014","journal-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Medi. Imaging"},{"key":"B21","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw"},{"key":"B22","author":"Haykin","year":"1994","journal-title":"Neural Networks: A Comprehensive Foundation."},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","article-title":"\u201cDeep residual learning for image recognition,\u201d","author":"He","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B24","article-title":"Neural networks for machine learning lecture 6a overview of mini-batch gradient descent","author":"Hinton","year":"2012","journal-title":"Cited"},{"key":"B25","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1038\/s41467-019-12898-9","article-title":"Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning","volume":"10","author":"Ho","year":"2019","journal-title":"Nat. Commun."},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017","journal-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12976-018-0093-x","article-title":"Novel neural network application for bacterial colony classification","volume":"15","author":"Huang","year":"2018","journal-title":"Theoretical Biology and Medical Modelling"},{"key":"B28","doi-asserted-by":"publisher","first-page":"V7","DOI":"10.1109\/ICCET.2010.5485542","article-title":"Analysis of Hu's moment invariants on image scaling and rotation","volume":"7","author":"Huang","year":"2010","journal-title":"2010 2nd international conference on computer engineering and technology"},{"key":"B29","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science."},{"key":"B30","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1504\/IJRIS.2019.102610","article-title":"Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset","volume":"11","author":"Khalifa","year":"2019","journal-title":"International Journal of Reasoning-based Intelligent Systems"},{"key":"B31","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv preprint arXiv:"},{"key":"B32","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B33","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/0146-664X(82)90034-X","article-title":"Elliptic Fourier features of a closed contour","volume":"18","author":"Kuhl","year":"1982","journal-title":"Computer graphics and image processing"},{"key":"B34","doi-asserted-by":"publisher","first-page":"8896","DOI":"10.1021\/acs.analchem.8b01024","article-title":"FT-IR hyperspectral imaging and artificial neural network analysis for identification of pathogenic bacteria","volume":"90","author":"Lasch","year":"2018","journal-title":"Anal. Chem."},{"key":"B35","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10233005","article-title":"Small-scale depthwise separable convolutional neural networks for bacteria classification","author":"Mai","year":"2021","journal-title":"Electronics"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-668667\/v1","article-title":"AGAR a microbial colony dataset for deep learning detection","author":"Majchrowska","year":"2021","journal-title":"arXiv preprint arXiv:"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1038\/srep25265","article-title":"An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information","author":"Melendez","year":"2016","journal-title":"Sci. Rep."},{"key":"B39","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief. Bioinformatics"},{"key":"B40","author":"Nair","year":"2010"},{"key":"B41","doi-asserted-by":"publisher","author":"Nasip","year":"2018","DOI":"10.1109\/ISMSIT.2018.8566685"},{"key":"B42","doi-asserted-by":"publisher","author":"Pak","year":"2017","DOI":"10.1109\/CAIPT.2017.8320684"},{"key":"B43","article-title":"Bacterial colony classification using atrous convolution with transfer learning","author":"Patel","year":"2021","journal-title":"Ann. Rom. Soc. Cell Biol."},{"key":"B44","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1016\/j.cmi.2020.02.006","article-title":"Machine learning in the clinical microbiology laboratory: has the time come for routine practice?","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clinical Microbiology and Infection"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2019.00827","article-title":"Application of machine learning in microbiology","author":"Qu","year":"2019","journal-title":"Front. Microbiol."},{"key":"B46","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-8176-4542-7_18","article-title":"Neural networks: an application for predicting smear negative pulmonary tuberculosis","author":"Santos","year":"2007","journal-title":"Advances in statistical methods for the health sciences: applications to cancer and AIDS studies, genome sequence analysis, and survival analysis"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: a survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evol. Intell."},{"key":"B49","doi-asserted-by":"publisher","first-page":"e24557","DOI":"10.5812\/ircmj.17(4)2015.24557","article-title":"Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system","volume":"17","author":"Saybani","year":"2015","journal-title":"Iran Red Crescent Med. J."},{"key":"B50","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv preprint arXiv:"},{"key":"B51","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/0304-3991(81)90061-9","article-title":"Bilinear interpolation of digital images","volume":"6","author":"Smith","year":"1981","journal-title":"Ultramicroscopy"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016","journal-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015","journal-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"B54","article-title":"An automated deep learning approach for bacterial image classification","author":"Talo","year":"2019","journal-title":"arXiv preprint arXiv:"},{"key":"B55","author":"Watkins","year":"2001","journal-title":"AIRS: A resource-limited artificial immune classifier"},{"key":"B56","doi-asserted-by":"publisher","first-page":"4550","DOI":"10.1109\/TNNLS.2017.2766168","article-title":"Deep learning in microscopy image analysis: A survey","volume":"29","author":"Xing","year":"2017","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"B57","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2014.2359471","article-title":"Scene recognition by manifold regularized deep learning architecture","volume":"26","author":"Yuan","year":"2015","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"B58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-015-0601-5","article-title":"In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology","volume":"16","author":"Yusuf","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"B59","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhaung","year":"2020","journal-title":"Proc. IEEE"},{"key":"B60","doi-asserted-by":"publisher","author":"Zheng","year":"2016","DOI":"10.1109\/CISP-BMEI.2016.7852772"},{"key":"B61","doi-asserted-by":"publisher","first-page":"e0184554","DOI":"10.1371\/journal.pone.0184554","article-title":"Deep learning approach to bacterial colony classification","volume":"12","author":"Zieli\u0144ski","year":"2017","journal-title":"PLoS ONE"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1200994\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:47:32Z","timestamp":1697701652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1200994\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":61,"alternative-id":["10.3389\/frai.2023.1200994"],"URL":"https:\/\/doi.org\/10.3389\/frai.2023.1200994","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,19]]},"article-number":"1200994"}}