{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T03:33:27Z","timestamp":1776569607733,"version":"3.51.2"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009192","name":"Alberta Innovates Accelerating Innovations into Care\u2014Concepts program","doi-asserted-by":"publisher","award":["AICE-Concepts 597389"],"award-info":[{"award-number":["AICE-Concepts 597389"]}],"id":[{"id":"10.13039\/501100009192","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2025-05253"],"award-info":[{"award-number":["RGPIN-2025-05253"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Yeast infections are a major concern in clinical settings, and several known species are recognized for their antifungal drug resistance, especially the multidrug-resistant pathogen Candidozyma auris. It is of increasing importance to identify pathogenic yeasts to improve treatment outcomes. We present a technique to identify these yeast pathogens using machine learning with a neural network (DenseNet-201) on images obtained from laser light scattering and conventional microscopy. We performed the binary classification of seven species of pathogenic yeast based on their light scattering patterns and their microscopy images. We achieved an average classification accuracy of 95.3% for light scattering patterns and 96.6% for microscopy images of the yeast cells. We also demonstrate high classification accuracy when isolating Candidozyma auris images from all other species combined, at an average of 95.1% for light scattering patterns and 96.7% for microscopy images. The high average classification accuracies suggest that both light scattering and microscopy image data can be combined with machine learning models to classify pathogenic yeasts.<\/jats:p>","DOI":"10.3390\/jimaging12030136","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T11:50:36Z","timestamp":1773921036000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Assisted Classification of Pathogenic Yeasts Using Laser Light Scattering and Conventional Microscopy"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0101-9494","authenticated-orcid":false,"given":"Xiaoxuan","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0367-7514","authenticated-orcid":false,"given":"Shamanth","family":"Shankarnarayan","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Alberta, Edmonton, AB T6G 2E1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zexi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manisha","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2660-3727","authenticated-orcid":false,"given":"Wojciech","family":"Rozmus","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Alberta, Edmonton, AB T6G 2E1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mrinal","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7426-1789","authenticated-orcid":false,"given":"Daniel A.","family":"Charlebois","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Alberta, Edmonton, AB T6G 2E1, Canada"},{"name":"Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-7790","authenticated-orcid":false,"given":"Ying Yin","family":"Tsui","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"165rv13","DOI":"10.1126\/scitranslmed.3004404","article-title":"Hidden Killers: Human Fungal Infections","volume":"4","author":"Brown","year":"2012","journal-title":"Sci. Transl. Med."},{"key":"ref_2","unstructured":"Jenks, J., and Tobin, E.H. (2025). Candidiasis. StatPearls, StatPearls Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103254","DOI":"10.1016\/j.fgb.2019.103254","article-title":"Facilitators of adaptation and antifungal resistance mechanisms in clinically relevant fungi","volume":"132","author":"Hokken","year":"2019","journal-title":"Fungal Genet. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"a019778","DOI":"10.1101\/cshperspect.a019778","article-title":"The Candida pathogenic species complex","volume":"4","author":"Turner","year":"2014","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ahmad, S., and Alfouzan, W. (2021). Candida auris: Epidemiology, Diagnosis, Pathogenesis, Antifungal Susceptibility, and Infection Control Measures to Combat the Spread of Infections in Healthcare Facilities. Microorganisms, 9.","DOI":"10.3390\/microorganisms9040807"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1111\/j.1348-0421.2008.00083.x","article-title":"Candida auris sp. nov., a novel ascomycetous yeast isolated from the external ear canal of an inpatient in a Japanese hospital","volume":"53","author":"Satoh","year":"2009","journal-title":"Microbiol. Immunol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.2147\/IDR.S402754","article-title":"Candida haemulonii Complex and Candida auris: Biology, Virulence Factors, Immune Response, and Multidrug Resistance","volume":"16","year":"2023","journal-title":"Infect. Drug Resist."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.jhin.2017.09.009","article-title":"Controlling a possible outbreak of Candida auris infection: Lessons learnt from multiple interventions","volume":"97","author":"Biswal","year":"2017","journal-title":"J. Hosp. Infect."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.diagmicrobio.2012.01.013","article-title":"Evaluation of the MALDI TOF-MS method for identification of Candida strains isolated from blood cultures","volume":"73","author":"Yaman","year":"2012","journal-title":"Diagn. Microbiol. Infect. Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2144\/000113672","article-title":"Extraction of genomic DNA from yeasts for PCR-based applications","volume":"50","author":"Kristjuhan","year":"2011","journal-title":"Biotechniques"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1111\/j.1365-2672.2004.02369.x","article-title":"New PCR-based methods for yeast identification","volume":"97","author":"Hierro","year":"2004","journal-title":"J. Appl. Microbiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e00883-24","DOI":"10.1128\/spectrum.00883-24","article-title":"Nosocomial transmission of fluconazole-resistant Candida glabrata bloodstream isolates revealed by whole-genome sequencing","volume":"12","author":"Hwang","year":"2024","journal-title":"Microbiol. Spectr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, X., Dong, Q., Zhou, Q., Fang, S., Xu, Y., Long, H., Chen, J., Li, X., Qin, H., and Mu, D. (2025). Genomics insights of candidiasis: Mechanisms of pathogenicity and drug resistance. Front. Microbiol., 16.","DOI":"10.3389\/fmicb.2025.1531543"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116589","DOI":"10.1016\/j.diagmicrobio.2024.116589","article-title":"Evaluation of usage of readily accessible Enterobacteriaceae differential and selective media for identifying Candida auris","volume":"111","author":"Khodadadi","year":"2025","journal-title":"Diagn. Microbiol. Infect. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e00326-20","DOI":"10.1128\/JCM.00326-20","article-title":"A Selective Medium for Isolation and Detection of Candida auris, an Emerging Pathogen","volume":"59","author":"Das","year":"2021","journal-title":"J. Clin. Microbiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12495","DOI":"10.1038\/s41598-019-48995-4","article-title":"Deep learning to improve breast cancer detection on screening mammography","volume":"9","author":"Shen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Arunachalam, H.B., Mishra, R., Daescu, O., Cederberg, K., Rakheja, D., Sengupta, A., Leonard, D., Hallac, R., and Leavey, P. (2019). Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210706"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"myad134","DOI":"10.1093\/mmy\/myad134","article-title":"Machine learning to identify clinically relevant Candida yeast species","volume":"62","author":"Shankarnarayan","year":"2024","journal-title":"Med. Mycol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1002\/jrs.6763","article-title":"Rapid Identification of Candida auris by Raman Spectroscopy Combined With Deep Learning","volume":"56","author":"Koya","year":"2025","journal-title":"J. Raman Spectrosc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1364\/BOE.424357","article-title":"Integration of light scattering with machine learning for label free cell detection","volume":"12","author":"Wan","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"28877","DOI":"10.1364\/OE.24.028877","article-title":"Physical characterization of hematopoietic stem cells using multidirectional label-free light scatterings","volume":"24","author":"Shahin","year":"2016","journal-title":"Opt. Express"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1364\/OE.19.000387","article-title":"Microscope-based label-free microfluidic cytometry","volume":"19","author":"Su","year":"2011","journal-title":"Opt. Express"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"067003","DOI":"10.1117\/1.3583577","article-title":"Label-free and noninvasive optical detection of the distribution of nanometer-size mitochondria in single cells","volume":"16","author":"Su","year":"2011","journal-title":"J. Biomed. Opt."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1002\/cyto.a.24771","article-title":"Multi-wavelength multi-direction laser light scattering for cell characterization using machine learning-based methods","volume":"103","author":"Liu","year":"2023","journal-title":"Cytometry Part A"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1002\/cyto.a.23865","article-title":"Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning","volume":"97","author":"Su","year":"2020","journal-title":"Cytometry Part A"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1364\/BOE.510022","article-title":"Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis","volume":"15","author":"Liu","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017). Densely connected convolutional networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zia-Ur-Rehman, M.K., Awang, M.K., Rashid, J., Ali, G., Hamid, M., Mahmoud, S.F., Saleh, D.I., and Ahmad, H.I. (2024). Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0304995"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e34420","DOI":"10.1016\/j.heliyon.2024.e34420","article-title":"MedCapsNet: A modified Densenet201 model integrated with capsule network for heel disease detection and classification","volume":"10","author":"Taher","year":"2024","journal-title":"Heliyon"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1002\/ima.22812","article-title":"A deep learning approach for classification of COVID and pneumonia using DenseNet-201","volume":"33","author":"Sanghvi","year":"2023","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gill, K.S., Anand, V., Gupta, S., and Figat, P. (2023). Stratification of White Blood Cells Using Optimized DenseNet201 Model. Communications in Computer and Information Science, Springer. Volume 1749.","DOI":"10.1007\/978-3-031-25088-0_3"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"27:1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_35","first-page":"1871","article-title":"LIBLINEAR: A Library for Large Linear Classification","volume":"9","author":"Fan","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1111\/j.1574-6976.2011.00278.x","article-title":"Candida glabrata, Candida parapsilosis and Candida tropicalis: Biology, epidemiology, pathogenicity and antifungal resistance","volume":"36","author":"Silva","year":"2012","journal-title":"FEMS Microbiol. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41426-018-0187-x","article-title":"Filamentation in Candida auris, an emerging fungal pathogen of humans: Passage through the mammalian body induces a heritable phenotypic switch","volume":"7","author":"Yue","year":"2018","journal-title":"Emerg. Microbes Infect."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e00779-21","DOI":"10.1128\/Spectrum.00779-21","article-title":"Phenotypic Switching and Filamentation in Candida haemulonii, an Emerging Opportunistic Pathogen of Humans","volume":"9","author":"Deng","year":"2021","journal-title":"Microbiol. Spectr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1099\/00222615-41-5-295","article-title":"Candida krusei: Biology, epidemiology, pathogenicity and clinical manifestations of an emerging pathogen","volume":"41","author":"Samaranayake","year":"1994","journal-title":"J. Med. Microbiol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guthrie, J.D., Shankarnarayan, S.A., and Charlebois, D.A. (2025). Peering inside the black box: Explainable AI to interpret advanced computer vision fungal pathogen prediction. bioRxiv.","DOI":"10.1101\/2025.06.27.662051"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2016). Grad-cam: Visual explanations from deep networks via gradient-based localization. arXiv.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hanafy, M., Khalil, M., and Abbas, H. (2018, January 19\u201321). Combining Classical and Deep Learning Methods for Twitter Sentiment Analysis. Proceedings of the ANNPR 2018, Siena, Italy. Volume 11081.","DOI":"10.1007\/978-3-319-99978-4_22"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/3\/136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:27:12Z","timestamp":1773923232000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/3\/136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,19]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["jimaging12030136"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12030136","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,19]]}}}