{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T06:34:34Z","timestamp":1791441274780,"version":"4.3.3"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>(1) Background: Helminth infections are a widespread global health concern, with Ascaris and taeniasis representing two of the most prevalent infestations. Traditional diagnostic methods, such as egg-based microscopy, are fraught with challenges, including subjectivity and low throughput, often leading to misdiagnosis. This study evaluates the efficacy of advanced deep learning models in accurately classifying Ascaris lumbricoides and Taenia saginata eggs from microscopic images, proposing a technologically enhanced approach for diagnostics in clinical settings. (2) Methods: Three state-of-the-art deep learning models, ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S, are considered. A diverse dataset comprising images of Ascaris, Taenia, and uninfected eggs was utilized for training and validating these models by performing multiclass experiments. (3) Results: All models demonstrated high classificatory accuracy, with ConvNeXt Tiny achieving an F1-score of 98.6%, followed by EfficientNet V2 S at 97.5% and MobileNet V3 S at 98.2% in the experiments. These results prove the potential of deep learning in streamlining and improving the diagnostic process for helminthic infections. The application of deep learning models such as ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S shows promise for efficient and accurate helminth egg classification, potentially significantly enhancing the diagnostic workflow. (4) Conclusion: The study demonstrates the feasibility of leveraging advanced computational techniques in parasitology and points towards a future where rapid, objective, and reliable diagnostics are standard.<\/jats:p>","DOI":"10.3390\/jpm15030121","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T05:03:59Z","timestamp":1742447039000},"page":"121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Comparative Evaluation of Deep Learning Models for Diagnosis of Helminth Infections"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2248-5996","authenticated-orcid":false,"given":"Omid","family":"Mirzaei","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia 99138, TRNC, Mersin 10, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0099-7544","authenticated-orcid":false,"given":"Ahmet","family":"Ilhan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Near East University, Nicosia 99138, TRNC, Mersin 10, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1635-0051","authenticated-orcid":false,"given":"Emrah","family":"Guler","sequence":"additional","affiliation":[{"name":"Department of Molecular Biology and Genetics, Faculty of Arts and Sciences, European University of Lefke, Lefke 99010, TRNC, Mersin 10, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2565-3425","authenticated-orcid":false,"given":"Kaya","family":"Suer","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Near East University, Nicosia 99138, TRNC, Mersin 10, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7284-1173","authenticated-orcid":false,"given":"Boran","family":"Sekeroglu","sequence":"additional","affiliation":[{"name":"Department of Information Systems Engineering, Faculty of Engineering, Near East University, Nicosia 99138, TRNC, Mersin 10, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Martviset, P., Phadungsil, W., Na-Bangchang, K., Sungkhabut, W., Panupornpong, T., Prathaphan, P., Torungkitmangmi, N., Chaimon, S., Wangboon, C., and Jamklang, M. (2023). Current prevalence and geographic distribution of helminth infections in the parasitic endemic areas of rural Northeastern Thailand. BMC Public Health, 23.","DOI":"10.1186\/s12889-023-15378-4"},{"key":"ref_2","first-page":"217","article-title":"The case of tapeworm uttered from Mouth","volume":"12","author":"Ay","year":"2016","journal-title":"Med. J. Bakirkoy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5578\/kvj.22108","article-title":"Foodborne Helminths","volume":"9","author":"Kozan","year":"2016","journal-title":"Kocatepe Vet. J."},{"key":"ref_4","unstructured":"World Health Organization (WHO) (2024, August 22). Soil-Transmitted Helminth Infections. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/soil-transmitted-helminth-infections."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alemu, Y., Degefa, T., Bajiro, M., and Teshome, G. (2022). Prevalence and intensity of soil-transmitted helminths infection among individuals in model and non-model households, South West Ethiopia: A comparative cross-sectional community based study. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0276137"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Akanksha, K., Kumari, A., Dutta, O., Prasanth, A., Deeba, F., and Salam, N. (2023). Prevalence of soil-transmitted helminth infections in HIV patients: A systematic review and meta-analysis. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-38030-y"},{"key":"ref_7","first-page":"128","article-title":"Epidemiology of intestinal parasites in a university hospital in Northern Cyprus: A 4-year retrospective experience","volume":"45","year":"2021","journal-title":"Epidemiology"},{"key":"ref_8","unstructured":"Centers for Disease Control and Prevention (CDC) (2024, August 21). Parasites-Ascariasis, Available online: https:\/\/www.cdc.gov\/sth\/about\/ascariasis.html?CDC_AAref_Val=https:\/\/www.cdc.gov\/parasites\/ascariasis\/index.html."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6648038","DOI":"10.1155\/2021\/6648038","article-title":"Deep learning approach for Ascaris lumbricoides parasite egg classification","volume":"2021","author":"Butploy","year":"2021","journal-title":"J. Parasitol. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1017\/S0031182021001256","article-title":"Ascaris lumbricoides eggs or artefacts? A diagnostic conundrum","volume":"148","author":"Maurelli","year":"2021","journal-title":"Parasitology"},{"key":"ref_11","unstructured":"Centers for Disease Control and Prevention (CDC) (2024, August 21). Taeniasis FAQs, Available online: https:\/\/www.cdc.gov\/taeniasis\/about\/?CDC_AAref_Val=https:\/\/www.cdc.gov\/parasites\/taeniasis\/gen_info\/faqs.html."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2147\/RMHP.S116545","article-title":"Human taeniasis: Current insights into prevention and management strategies in endemic countries","volume":"10","author":"Okello","year":"2017","journal-title":"Risk Manag. Healthc. Policy"},{"key":"ref_13","unstructured":"Centers for Disease Control and Prevention (CDC) (2024, August 27). Laboratory Identification of Parasites of Public Health Concern. Taeniasis, Image Gallery, Available online: https:\/\/www.cdc.gov\/dpdx\/taeniasis\/index.html#:~:text=The%20eggs%20of%20Taenia%20spp,oncosphere%20contains%20six%20refractile%20hooks."},{"key":"ref_14","first-page":"66","article-title":"Basic Processing Models of Artificial Intelligence In Clinical Microbiology Laboratories","volume":"3","author":"Sahin","year":"2019","journal-title":"J. Biotechnol. Strateg. Health Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mustapha, M.T., Ozsahin, D.U., Ozsahin, I., and Uzun, B. (2022). Breast cancer screening based on supervised learning and multi-criteria decision-making. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061326"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"\u00d6zbilge, E., G\u00fcler, E., G\u00fcvenir, M., Sanl\u0131dag, T., Ozbilgin, A., and Suer, K. (2021). Automated malaria parasite detection using artificial neural network. International Conference on Theory and Applications of Fuzzy Systems and Soft Computing, Springer.","DOI":"10.1007\/978-3-030-64058-3_78"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"678","DOI":"10.3855\/jidc.13532","article-title":"Automated detection of Mycobacterium tuberculosis using transfer learning","volume":"15","author":"Ibrahim","year":"2021","journal-title":"J. Infect. Dev. Ctries"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Goodswen, S.J., Barratt, J.L.N., Kennedy, P.J., Kaufer, A., Calarco, L., and Ellis, J.T. (2021). Machine learning and applications in microbiology. FEMS Microbiol. Rev., 45.","DOI":"10.1093\/femsre\/fuab015"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.tim.2021.01.006","article-title":"Deep Learning for Imaging and Detection of Microorganisms","volume":"29","author":"Zhang","year":"2021","journal-title":"Trends Microbiol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, W., Zhu, H., Geng, J., Hu, X., Li, Y., Shi, H., Wang, Y., Zhu, D., Wang, H., and Xie, L. (2024). Recognition of parasitic helminth eggs via a deep learning-based platform. Front. Microbiol., 15.","DOI":"10.3389\/fmicb.2024.1485001"},{"key":"ref_21","first-page":"890","article-title":"Automatic detection of Ascaris lumbricoides in microscopic images using convolutional neural networks (CNN)","volume":"15","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Muthulakshm, M., Venkatesan, K., Rahayu, S.B., and Elangovan, K. (2024, January 7\u20138). A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs. Proceedings of the Internationa\u015f Visualization, Informatics and Technology Conference (IVIT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IVIT62102.2024.10692960"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Abdelmula, A.M., Mirzaei, O., G\u00fcler, E., and S\u00fcer, K. (2023). Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images. Diagnostics, 14.","DOI":"10.3390\/diagnostics14010012"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_25","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_26","unstructured":"Tan, M., and Le, Q. (2021, January 18\u201324). Efficientnetv2: Smaller models and faster training. Proceedings of the 38th International Conference on Machine Learning, Virtual."},{"key":"ref_27","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_29","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Demsar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jss.2007.02.053","article-title":"Analysis of variance: Is there a difference in means and what does it mean?","volume":"144","author":"Kao","year":"2008","journal-title":"J. Surg. Res."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/3\/121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:56:58Z","timestamp":1760029018000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/3\/121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,20]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["jpm15030121"],"URL":"https:\/\/doi.org\/10.3390\/jpm15030121","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,20]]}}}