{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:00:27Z","timestamp":1776938427935,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007751","name":"Akademia G\u00f3rniczo-Hutnicza im. Stanislawa Staszica","doi-asserted-by":"publisher","award":["16.16.120.773"],"award-info":[{"award-number":["16.16.120.773"]}],"id":[{"id":"10.13039\/501100007751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R56 AI127455"],"award-info":[{"award-number":["R56 AI127455"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Clostridioides difficile infection (CDI) is an enteric bacterial disease that is increasing in incidence worldwide. Symptoms of CDI range from mild diarrhea to severe life-threatening inflammation of the colon. While antibiotics are standard-of-care treatments for CDI, they are also the biggest risk factor for development of CDI and recurrence. Therefore, novel therapies that successfully treat CDI and protect against recurrence are an unmet clinical need. Screening for novel drug leads is often tested by manual image analysis. The process is slow, tedious and is subject to human error and bias. So far, little work has focused on computer-aided screening for drug leads based on fluorescence images. Here, we propose a novel method to identify characteristic morphological changes in human fibroblast cells exposed to C. difficile toxins based on computer vision algorithms supported by deep learning methods. Classical image processing algorithms for the pre-processing stage are used together with an adjusted pre-trained deep convolutional neural network responsible for cell classification. In this study, we take advantage of transfer learning methodology by examining pre-trained VGG-19, ResNet50, Xception, and DenseNet121 convolutional neural network (CNN) models with adjusted, densely connected classifiers. We compare the obtained results with those of other machine learning algorithms and also visualize and interpret them. The proposed models have been evaluated on a dataset containing 369 images with 6112 cases. DenseNet121 achieved the highest results with a 93.5% accuracy, 92% sensitivity, and 95% specificity, respectively.<\/jats:p>","DOI":"10.3390\/s20236713","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T09:06:28Z","timestamp":1606208788000},"page":"6713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7713-526X","authenticated-orcid":false,"given":"Andrzej","family":"Brodzicki","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0146-8652","authenticated-orcid":false,"given":"Joanna","family":"Jaworek-Korjakowska","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6652-7978","authenticated-orcid":false,"given":"Pawel","family":"Kleczek","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5622-5095","authenticated-orcid":false,"given":"Megan","family":"Garland","sequence":"additional","affiliation":[{"name":"Cancer Biology Program, Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3753-4412","authenticated-orcid":false,"given":"Matthew","family":"Bogyo","sequence":"additional","affiliation":[{"name":"Cancer Biology Program, Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA"},{"name":"Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA"},{"name":"Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Smits, W.K., Lyras, D., Lacy, D.B., and Wilcox, M.H. (2016). Clostridium difficile infection. Nat. Rev. Dis. Prim., 2.","DOI":"10.1038\/nrdp.2016.20"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Czepiel, J., Dr\u00f3\u017cd\u017c, M., Pituch, H., Kuijper, E.J., Perucki, W., Mielimonka, A., Goldman, S., Wulta\u0144ska, D., Garlicki, A., and Biesiada, G. (2010). Clostridium difficile infection: Review. Eur. J. Clin. Microbiol. Infect. Dis., 38.","DOI":"10.1007\/s10096-019-03539-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1016\/j.ajic.2014.06.011","article-title":"The rise in Clostridium difficile infection incidence among hospitalized adults in the United States: 2001\u20132010","volume":"42","author":"Reveles","year":"2014","journal-title":"Am. J. Infect. Control"},{"key":"ref_4","unstructured":"Centers for Disease Control and Prevention (2011). QuickStats: Rates of Clostridium Difficile Infection among Hospitalized Patients Aged 65 Years, Age Group\u2014National Hospital Discharge Survey, 1996\u20132009. MMWR Morb. Mortal. Wkly. Rep., 60, 1171."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S65","DOI":"10.1093\/cid\/cis319","article-title":"Current Status of Clostridium difficile Infection Epidemiology","volume":"55","author":"Lessa","year":"2012","journal-title":"Clin. Infect. Dis."},{"key":"ref_6","unstructured":"Butler, M., Olson, A., Drekonja, D.M., Shaukat, A., Schwehr, N.A., Shippee, N.D., and Wilt, T.J. (2016). Early Diagnosis, Prevention, and Treatment of Clostridium Difficile: Update, NCBI 5600 Fishers Lane."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1056\/NEJMoa1408913","article-title":"Burden of Clostridium difficile infection in the United States","volume":"372","author":"Lessa","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Borren, N.Z., Ghadermarzi, S., Hutfless, S.M., and Ananthakrishnan, A.N. (2017). The emergence of Clostridium difficile infection in Asia: A systematic review and meta-analysis of incidence and impact. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176797"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"010407","DOI":"10.7189\/jogh.09.010407","article-title":"Global burden of Clostridium difficile infections: A systematic review and meta-analysis","volume":"9","author":"Balsells","year":"2019","journal-title":"J. Glob. Health"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e1917597","DOI":"10.1001\/jamanetworkopen.2019.17597","article-title":"Incidence and Outcomes Associated with Clostridium difficile Infections","volume":"3","author":"Marra","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/fcimb.2012.00028","article-title":"Toward a structural understanding of Clostridium difficile toxins A and B","volume":"2","author":"Pruitt","year":"2012","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3109\/03009734.2014.896437","article-title":"Discovery and preclinical development of new antibiotics","volume":"119","author":"Hughes","year":"2014","journal-title":"Upsala J. Med. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Garland, M., Jaworek-Korjakowska, J., Libal, U., and Bogyo, M. (2018). An Automatic Analysis System for High-Throughput Clostridium Difficile Toxin Activity Screening. Appl. Sci., 8.","DOI":"10.3390\/app8091512"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ogiela, L., Tadeusiewicz, R., and Ogiela, M. (2006, January 26\u201327). Cognitive Approach to Visual Data Interpretation in Medical Information and Recognition Systems. Proceedings of the International Workshop on Intelligent Computing in Pattern Analysis and Synthesis, Xi\u2019an, China.","DOI":"10.1007\/11821045_26"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"165","DOI":"10.2478\/amcs-2014-0013","article-title":"Approximation of phenol concentration using novel hybrid computational intelligence methods","volume":"24","author":"Plawiak","year":"2014","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, B.Y., Oh, J., Young, V.B., Rao, K., and Wiens, J. (2019). Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection. Open Forum Infect. Dis., 6.","DOI":"10.1093\/ofid\/ofz186"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115104","DOI":"10.1016\/j.diagmicrobio.2020.115104","article-title":"Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital","volume":"98","author":"Marra","year":"2020","journal-title":"Diagn. Microbiol. Infect. Dis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shi, Y., Suk, H.I., and Liu, M. (2018). SoLiD: Segmentation of Clostridioides Difficile Cells in the Presence of Inhomogeneous Illumination Using a Deep Adversarial Network. Machine Learning in Medical Imaging, Springer International Publishing.","DOI":"10.1007\/978-3-030-00919-9"},{"key":"ref_19","unstructured":"MATLAB (2018). Version 9.4 (R2018b), The MathWorks Inc."},{"key":"ref_20","unstructured":"Chollet, F. (2020, November 24). keras, GitHub. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/2151237X.2007.10129236","article-title":"Adaptive Thresholding using the Integral Image","volume":"12","author":"Bradley","year":"2007","journal-title":"J. Graph. Tools"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0165-1684(94)90060-4","article-title":"Topographic distance and watershed lines","volume":"38","author":"Meyer","year":"1994","journal-title":"Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3233\/FI-2000-411207","article-title":"The Watershed Transform: Definitions, Algorithms and Parallelization Strategies","volume":"41","author":"Roerdink","year":"2000","journal-title":"Fundam. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: Review, opportunities and challenges","volume":"19","author":"Miotto","year":"2017","journal-title":"Briefings Bioinform."},{"key":"ref_27","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_28","unstructured":"Gron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media, Inc.. [1st ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_30","unstructured":"Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M.M.A., Yang, Y., and Zhou, Y. (2017). Deep Learning Scaling is Predictable, Empirically. arXiv."},{"key":"ref_31","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. (IJCV)"},{"key":"ref_32","unstructured":"Krizhevsky, A., Sutskever, I.E., and Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Inf. Process. Syst., 25."},{"key":"ref_33","unstructured":"Jordan, J. (2020, November 24). Common Architectures in Convolutional Neural Networks. Available online: https:\/\/www.jeremyjordan.me\/convnet-architectures\/."},{"key":"ref_34","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","unstructured":"Xu, J. (2020, November 24). Towardsdatascience: An Intuitive Guide to Deep Network Architectures. Available online: https:\/\/towardsdatascience.com\/an-intuitive-guide-to-deep-network-architectures-65fdc477db41."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2016). Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_40","unstructured":"Bendersky, E. (2020, November 24). Depthwise Separable Convolutions for Machine Learning. Available online: https:\/\/openreview.net\/pdf?id=S1jBcueAb."},{"key":"ref_41","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014, January 14\u201316). Network In Network. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_42","unstructured":"Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation Functions: Comparison of trends in Practice and Research for Deep Learning. arXiv."},{"key":"ref_43","unstructured":"Sharma, S. (2020, November 24). Towards Data Science: Activation Functions in Neural Networks. Available online: https:\/\/towardsdatascience.com\/activation-functions-in-neural-networks-eb8c1ba565f8."},{"key":"ref_44","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","unstructured":"Mack, D. (2020, November 24). How to Pick the Best Learning Rate for your Machine Learning Project. Available online: https:\/\/medium.com\/octavian-ai\/which-optimizer-and-learning-rate-should-i-use-for-deep-learning-5acb418f9b2."},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_47","unstructured":"Buhrmester, V., M\u00fcnch, D., and Arens, M. (2019). Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey. arXiv."},{"key":"ref_48","unstructured":"Tjoa, E., and Guan, C. (2019). A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1145\/3236386.3241340","article-title":"The Mythos of Model Interpretability","volume":"16","author":"Lipton","year":"2018","journal-title":"ACM Queue"},{"key":"ref_50","unstructured":"Chollet, F. (2020, November 24). Deep Learning with Python. Available online: http:\/\/faculty.neu.edu.cn\/yury\/AAI\/Textbook\/Deep%20Learning%20with%20Python.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6713\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:36Z","timestamp":1760178996000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,24]]},"references-count":50,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236713"],"URL":"https:\/\/doi.org\/10.3390\/s20236713","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,24]]}}}