{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T04:03:54Z","timestamp":1792123434858,"version":"4.4.0"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ARDEB 1001 Program","award":["122E164"],"award-info":[{"award-number":["122E164"]}]},{"name":"ARDEB 1001 Program","award":["02025005002463"],"award-info":[{"award-number":["02025005002463"]}]},{"DOI":"10.13039\/501100005322","name":"Recep Tayyip Erdo\u011fan University Development Foundation","doi-asserted-by":"publisher","award":["122E164"],"award-info":[{"award-number":["122E164"]}],"id":[{"id":"10.13039\/501100005322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005322","name":"Recep Tayyip Erdo\u011fan University Development Foundation","doi-asserted-by":"publisher","award":["02025005002463"],"award-info":[{"award-number":["02025005002463"]}],"id":[{"id":"10.13039\/501100005322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Introduction: Fertility is fundamental to human well-being, significantly impacting both individual lives and societal development. In particular, sperm morphology\u2014referring to the shape, size, and structural integrity of sperm cells\u2014is a key indicator in diagnosing male infertility and selecting viable sperm in assisted reproductive technologies such as in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI). However, traditional manual evaluation methods are highly subjective and inconsistent, creating a need for standardized, automated systems. Objectives: This study aims to develop a robust and fully automated sperm morphology classification framework capable of accurately identifying a wide range of morphological abnormalities, thereby minimizing observer variability and improving diagnostic support in reproductive healthcare. Methods: We propose a novel ensemble-based classification approach that combines convolutional neural network (CNN)-derived features using both feature-level and decision-level fusion techniques. Features extracted from multiple EfficientNetV2 variants are fused and classified using Support Vector Machines (SVM), Random Forest (RF), and Multi-Layer Perceptron with Attention (MLP-Attention). Decision-level fusion is achieved via soft voting to enhance robustness and accuracy. Results: The proposed ensemble framework was evaluated using the Hi-LabSpermMorpho dataset, which contains 18 distinct sperm morphology classes. The fusion-based model achieved an accuracy of 67.70%, significantly outperforming individual classifiers. The integration of multiple CNN architectures and ensemble techniques effectively mitigated class imbalance and enhanced the generalizability of the model. Conclusions: The presented methodology demonstrates a substantial improvement over traditional and single-model approaches in automated sperm morphology classification. By leveraging ensemble learning and multi-level fusion, the model provides a reliable and scalable solution for clinical decision-making in male fertility assessment.<\/jats:p>","DOI":"10.3390\/diagnostics15121564","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T06:11:07Z","timestamp":1750313467000},"page":"1564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0746-7693","authenticated-orcid":false,"given":"Abdulsamet","family":"Aktas","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Marmara University, 34840 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7891-0181","authenticated-orcid":false,"given":"Taha","family":"Cap","sequence":"additional","affiliation":[{"name":"Department of Control and Automation Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4591-7368","authenticated-orcid":false,"given":"Gorkem","family":"Serbes","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1753-2703","authenticated-orcid":false,"given":"Hamza Osman","family":"Ilhan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5189-3166","authenticated-orcid":false,"given":"Hakk\u0131","family":"Uzun","sequence":"additional","affiliation":[{"name":"Department of Urology, Faculty of Medicine, Recep Tayyip Erdo\u011fan University, 53020 Rize, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1080\/09513590.2017.1391208","article-title":"Infertility and surrogacy first mentioned on a 4000-year-old Assyrian clay tablet of marriage contract in Turkey","volume":"34","author":"Turp","year":"2018","journal-title":"Gynecol. Endocrinol."},{"key":"ref_2","unstructured":"World Health Organization (2023). Infertility Prevalence Estimates, 1990\u20132021, World Health Organization."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1046\/j.1365-2605.2002.00355.x","article-title":"Sperm morphology analysis using strict criteria as a prognostic factor in intrauterine insemination","volume":"25","author":"Lee","year":"2002","journal-title":"Int. J. Androl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/j.1939-4640.1986.tb00913.x","article-title":"Morphometric analysis of spermatozoa in the assessment of human male fertility","volume":"7","author":"Katz","year":"1986","journal-title":"J. Androl."},{"key":"ref_5","first-page":"97","article-title":"Standards for the rating of human sperm morphology. A cooperative study","volume":"11","author":"Freund","year":"1966","journal-title":"Int. J. Fertil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1136\/bmj.304.6840.1491","article-title":"Statistical methods for assessing observer variability in clinical measures","volume":"304","author":"Brennan","year":"1992","journal-title":"BMJ Br. Med J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.compbiomed.2017.10.009","article-title":"A dictionary learning approach for human sperm heads classification","volume":"91","author":"Shaker","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.5603\/GP.a2017.0012","article-title":"Manual vs. computer-assisted sperm analysis: Can CASA replace manual assessment of human semen in clinical practice?","volume":"88","author":"Berger","year":"2017","journal-title":"Ginekol. Pol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.cmpb.2015.08.013","article-title":"An efficient method for automatic morphological abnormality detection from human sperm images","volume":"122","author":"Ghasemian","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Aktas, A., Serbes, G., and Ilhan, H.O. (2023, January 13\u201315). The Performance Analysis of Convolutional Neural Networks and Vision Transformers in the Classification of Sperm Morphology. Proceedings of the 2023 8th International Conference on Computer Science and Engineering (UBMK), Burdur, Turkey.","DOI":"10.1109\/UBMK59864.2023.10286635"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.compbiomed.2019.04.030","article-title":"A novel deep learning method for automatic assessment of human sperm images","volume":"109","author":"Javadi","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2200111","DOI":"10.1002\/aisy.202200111","article-title":"Ensembled Deep Learning for the Classification of Human Sperm Head Morphology","volume":"4","author":"Spencer","year":"2022","journal-title":"Adv. Intell. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ilhan, H.O., Serbes, G., and Aydin, N. (2020). Automated sperm morphology analysis approach using a directional masking technique. Comput. Biol. Med., 122.","DOI":"10.1016\/j.compbiomed.2020.103845"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ilhan, H.O., Serbes, G., and Aydin, N. (2018, January 4\u20136). Dual tree complex wavelet transform based sperm abnormality classification. Proceedings of the 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, Greece.","DOI":"10.1109\/TSP.2018.8441431"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Y\u00fczkat, M., Ilhan, H.O., and Aydin, N. (2021). Multi-model CNN fusion for sperm morphology analysis. Comput. Biol. Med., 137.","DOI":"10.1016\/j.compbiomed.2021.104790"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.compbiomed.2017.03.004","article-title":"Gold-standard for computer-assisted morphological sperm analysis","volume":"83","author":"Chang","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Riordon, J., McCallum, C., and Sinton, D. (2019). Deep learning for the classification of human sperm. Comput. Biol. Med., 111.","DOI":"10.1016\/j.compbiomed.2019.103342"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_20","first-page":"24261","article-title":"Mlp-mixer: An all-mlp architecture for vision","volume":"34","author":"Tolstikhin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"196070","DOI":"10.1109\/ACCESS.2024.3521643","article-title":"Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset with Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis","volume":"12","author":"Aktas","year":"2024","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.cmpb.2013.05.003","article-title":"Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ","volume":"111","author":"Alegre","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1016\/j.cmpb.2012.01.004","article-title":"Texture and moments-based classification of the acrosome integrity of boar spermatozoa images","volume":"108","author":"Alegre","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s11517-019-02101-y","article-title":"A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods","volume":"58","author":"Ilhan","year":"2020","journal-title":"Med Biol. Eng. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nissen, M.S., Krause, O., Almstrup, K., Kj\u00e6rulff, S., Nielsen, T.T., and Nielsen, M. (2017, January 12\u201314). Convolutional neural networks for segmentation and object detection of human semen. Proceedings of the Image Analysis: 20th Scandinavian Conference, SCIA 2017, Troms\u00f8, Norway. Proceedings, Part I 20.","DOI":"10.1007\/978-3-319-59126-1_33"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.compbiomed.2019.04.032","article-title":"Automatic segmentation of Sperm\u2019s parts in microscopic images of human semen smears using concatenated learning approaches","volume":"109","author":"Movahed","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ilhan, H.O., and Serbes, G. (2022). Sperm morphology analysis by using the fusion of two-stage fine-tuned deep networks. Biomed. Signal Process. Control, 71.","DOI":"10.1016\/j.bspc.2021.103246"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Iqbal, I., Mustafa, G., and Ma, J. (2020). Deep learning-based morphological classification of human sperm heads. Diagnostics, 10.","DOI":"10.3390\/diagnostics10050325"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.1007\/s00521-023-09346-7","article-title":"COVID-19 classification based on a deep learning and machine learning fusion technique using chest CT images","volume":"36","author":"Salama","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Verma, J., Kansal, I., Popli, R., Khullar, V., Singh, D., Snehi, M., and Kumar, R. (2024, January 7\u20138). A Hybrid Images Deep Trained Feature Extraction and Ensemble Learning Models for Classification of Multi Disease in Fundus Images. Proceedings of the Nordic Conference on Digital Health and Wireless Solutions, Oulu, Finland.","DOI":"10.1007\/978-3-031-59091-7_14"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122159","DOI":"10.1016\/j.eswa.2023.122159","article-title":"Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification","volume":"238","author":"Celik","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mabrouk, A., Diaz Redondo, R.P., Dahou, A., Abd Elaziz, M., and Kayed, M. (2022). Pneumonia detection on chest X-ray images using ensemble of deep convolutional neural networks. Appl. Sci., 12.","DOI":"10.3390\/app12136448"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Zha, X., Zhou, Y., Cao, Y., and Chen, D. (2022, January 28\u201331). Improving human sperm head morphology classification with unsupervised anatomical feature distillation. Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India.","DOI":"10.1109\/ISBI52829.2022.9761633"},{"key":"ref_34","unstructured":"World Health Organization (2021). WHO Laboratory Manual for the Examination and Processing of Human Semen, World Health Organization."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Basthikodi, M., Chaithrashree, M., Ahamed Shafeeq, B., and Gurpur, A.P. (2024). Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-77243-7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tiwari, P., Upadhyay, D., Pant, B., and Mohd, N. (2022, January 22\u201323). Multiclass classification of disease using cnn and svm of medical imaging. Proceedings of the International Conference on Advances in Computing and Data Sciences, Kurnool, India.","DOI":"10.1007\/978-3-031-12641-3_8"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17672","DOI":"10.3934\/mbe.2023786","article-title":"Improved support vector machine classification for imbalanced medical datasets by novel hybrid sampling combining modified mega-trend-diffusion and bagging extreme learning machine model","volume":"20","author":"Lin","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gupta, S., and Gupta, S. (2025). Feature Extraction and Feature Selection Procedures for Medical Image Analysis. Computer-Assisted Analysis for Digital Medicinal Imagery, IGI Global.","DOI":"10.4018\/979-8-3693-5226-7.ch010"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/JBHI.2023.3241439","article-title":"Medical image classification using light-weight CNN with spiking cortical model based attention module","volume":"27","author":"Zhou","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s10462-024-10897-x","article-title":"GAN based augmentation using a hybrid loss function for dermoscopy images","volume":"57","author":"Goceri","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"16498","DOI":"10.1109\/ACCESS.2024.3360215","article-title":"A GAN-based data augmentation method for imbalanced multi-class skin lesion classification","volume":"12","author":"Su","year":"2024","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112050","DOI":"10.1016\/j.asoc.2024.112050","article-title":"Leveraging GANs data augmentation for imbalanced medical image classification","volume":"165","author":"Ding","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_43","first-page":"1","article-title":"GAN-based Synthetic Medical Image Augmentation for Class Imbalanced Dermoscopic Image Analysis","volume":"33","author":"Alshardan","year":"2024","journal-title":"Fractals"},{"key":"ref_44","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Eshraghi, M.A., Ayatollahi, A., and Shokouhi, S.B. (2023). COV-MobNets: A mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images. BMC Med. Imaging, 23.","DOI":"10.1186\/s12880-023-01039-w"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vincent, A.C.S.R., and Sengan, S. (2024). Edge computing-based ensemble learning model for health care decision systems. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-78225-5"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"119900","DOI":"10.1016\/j.eswa.2023.119900","article-title":"Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images","volume":"223","author":"Hussein","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"109782","DOI":"10.1016\/j.engappai.2024.109782","article-title":"An ensemble based lightweight deep learning model for the prediction of cardiovascular diseases from electrocardiogram images","volume":"141","author":"Hasan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/12\/1564\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:54:52Z","timestamp":1760032492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/12\/1564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,19]]},"references-count":49,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["diagnostics15121564"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15121564","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,19]]}}}