{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T03:33:28Z","timestamp":1776569608399,"version":"3.51.2"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health","award":["T34GM140948"],"award-info":[{"award-number":["T34GM140948"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pathogens"],"abstract":"<jats:p>Pathogenic yeasts are an increasing concern in healthcare, with species like Candida auris often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identification is critical, especially in resource-limited settings. This study presents a convolutional neural network (CNN)-based approach for classifying pathogenic yeast species from microscopy images. Using transfer learning, we trained the model to identify six yeast species from simple micrographs, achieving high classification accuracy (93.91% at the patch level, 99.09% at the whole image level) and low misclassification rates across species, with the best performing model. Our pipeline offers a streamlined, cost-effective diagnostic tool for yeast identification, enabling faster response times in clinical environments and reducing reliance on costly and complex molecular methods.<\/jats:p>","DOI":"10.3390\/pathogens14050504","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T04:05:17Z","timestamp":1747800317000},"page":"504","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Complete Transfer Learning-Based Pipeline for Discriminating Between Select Pathogenic Yeasts from Microscopy Photographs"],"prefix":"10.3390","volume":"14","author":[{"given":"Ryan A.","family":"Parker","sequence":"first","affiliation":[{"name":"School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2321-7994","authenticated-orcid":false,"given":"Danielle S.","family":"Hannagan","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan H.","family":"Strydom","sequence":"additional","affiliation":[{"name":"School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher J.","family":"Boon","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jessica","family":"Fussell","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2433-5783","authenticated-orcid":false,"given":"Chelbie A.","family":"Mitchell","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katie L.","family":"Moerschel","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0159-8870","authenticated-orcid":false,"given":"Aura G.","family":"Valter-Franco","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5480-6235","authenticated-orcid":false,"given":"Christopher T.","family":"Cornelison","sequence":"additional","affiliation":[{"name":"BioInnovation Laboratory, Department of Molecular and Cellular Biology, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Parker, R.A., Gabriel, K.T., Graham, K., Butts, B.K., and Cornelison, C.T. (2022). Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens, 11.","DOI":"10.3390\/pathogens11080821"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Kordalewska, M., and Perlin, D.S. (2019). Identification of Drug Resistant Candida auris. Front. Microbiol., 10.","DOI":"10.3389\/fmicb.2019.01918"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"\u010cern\u00e1kov\u00e1, L., Roudbary, M., Br\u00e1s, S., Tafaj, S., and Rodrigues, C.F. (2021). Candida auris: A Quick Review on Identification, Current Treatments, and Challenges. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22094470"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jackson, B.R., Chow, N., Forsberg, K., Litvintseva, A.P., Lockhart, S.R., Welsh, R., Vallabhaneni, S., and Chiller, T. (2019). On the Origins of a Species: What Might Explain the Rise of Candida auris?. J. Fungi, 5.","DOI":"10.3390\/jof5030058"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fasciana, T., Cortegiani, A., Ippolito, M., Giarratano, A., Di Quattro, O., Lipari, D., Graceffa, D., and Giammanco, A. (2020). Candida auris: An Overview of How to Screen, Detect, Test and Control This Emerging Pathogen. Antibiotics, 9.","DOI":"10.3390\/antibiotics9110778"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Wu, Y., Gao, Y., Niu, X., Li, J., Tang, M., Fu, C., Qi, R., Song, B., and Chen, H. (2022). Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: A singled centered retrospective study. BMC Infect. Dis., 22.","DOI":"10.1186\/s12879-022-07125-8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, T.T., Xiao, J., Sohn, M.B., Fiscella, K.A., Gilbert, C., Grier, A., Gill, A.L., and Gill, S.R. (2021). Machine learning approach identified Multi-Platform Factors for caries prediction in Child-Mother dyads. Front. Cell. Infect. Microbiol., 11.","DOI":"10.3389\/fcimb.2021.727630"},{"key":"ref_9","unstructured":"CDC (2024, August 28). Identification of Candida auris|Candida auris|Fungal Diseases, Available online: https:\/\/www.cdc.gov\/fungal\/candida-auris\/identification.html."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Manteca, M.G., Ocampo-Sosa, A.A., Ruiz de Alegr\u00eda-Puig, C., P\u00eda Roiz, M., Rodr\u00edguez-Grande, J., Madrazo, F., Calvo, J., Rodr\u00edguez-Cobo, L., L\u00f3pez-Higuera, J.M., and Fari\u00f1as, M.C. (2023). Automatic classification of Candida species using Raman spectroscopy and machine learning. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 290.","DOI":"10.1016\/j.saa.2022.122270"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jeffery-Smith, A., Taori, S., Schelenz, S., Jeffery, K., Johnson, E., Borman, A.M., Candida auris Incident Management Team, Manuel, R., and Brown, C.S. (2017). Candida auris: A Review of the Literature. Clin. Microbiol. Rev., 31.","DOI":"10.1128\/CMR.00029-17"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","unstructured":"Krizhevsky, A., and Hinton, G. (2023, September 09). Learning Multiple Layers of Features from Tiny Images. University of Toronto. Available online: https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf."},{"key":"ref_14","unstructured":"Shaikh, J. (2024, April 01). Essentials of Deep Learning: Visualizing Convolutional Neural Networks in Python. Analytics Vidhya. Available online: https:\/\/www.analyticsvidhya.com\/blog\/2018\/03\/essentials-of-deep-learning-visualizing-convolutional-neural-networks."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1145\/2001269.2001295","article-title":"Unsupervised learning of hierarchical representations with convolutional deep belief networks","volume":"54","author":"Lee","year":"2011","journal-title":"Commun. ACM"},{"key":"ref_16","unstructured":"CDC (2023, August 08). How the AR Isolate Bank Helps Combat Antibiotic Resistance. Centers for Disease Control and Prevention, Available online: https:\/\/www.cdc.gov\/drugresistance\/resistance-bank\/index.html."},{"key":"ref_17","unstructured":"(2023, August 08). Welcome to the ARS Culture Collection (NRRL). ARS Culture Collection (NRRL), Available online: https:\/\/nrrl.ncaur.usda.gov\/."},{"key":"ref_18","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2016). Hyperband: A novel Bandit-Based approach to hyperparameter optimization. arXiv."},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for Large-Scale image recognition. arXiv."},{"key":"ref_20","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, A. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/BF00342633","article-title":"Cognitron: A self-organizing multilayered neural network","volume":"20","author":"Fukushima","year":"1975","journal-title":"Biol. Cybern."},{"key":"ref_23","unstructured":"D\u017eakula, N.B., and Bezdan, T. (2019, January 20). Convolutional Neural Network Layers and Architectures. Proceedings of the Sinteza 2019\u2014International Scientific Conference on Information Technology and Data Related Research, Novi Sad, Serbia."},{"key":"ref_24","unstructured":"(2024, August 28). Papers with Code\u2014ReLU Explained. Available online: https:\/\/paperswithcode.com\/method\/relu."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Howard, A., Pang, R., Adam, H., Le, Q.V., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., and Chu, G. (November, January 27). Searching for MobileNetV3. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_26","unstructured":"(2024, August 28). Papers with Code\u2014Hard Swish Explained. Available online: https:\/\/paperswithcode.com\/method\/hard-swish."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"}],"container-title":["Pathogens"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-0817\/14\/5\/504\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:36:09Z","timestamp":1760031369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-0817\/14\/5\/504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,21]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["pathogens14050504"],"URL":"https:\/\/doi.org\/10.3390\/pathogens14050504","relation":{},"ISSN":["2076-0817"],"issn-type":[{"value":"2076-0817","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,21]]}}}