{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T15:48:00Z","timestamp":1792856880837,"version":"4.5.0"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Research Council of Turkey (T\u00dcB\u0130TAK)","award":["02025005015491"],"award-info":[{"award-number":["02025005015491"]}],"id":[{"id":"https:\/\/ror.org\/04w9kkr77","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005322","name":"Recep Tayyip Erdo\u011fan University Development Foundation","doi-asserted-by":"publisher","award":["02025005015491"],"award-info":[{"award-number":["02025005015491"]}],"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: Sperm morphology is a fundamental parameter in the evaluation of male infertility, offering critical insights into reproductive health. However, traditional manual assessments under microscopy are limited by operator dependency and subjective interpretation caused by biological variation. To overcome these limitations, there is a need for accurate and fully automated classification systems. Objectives: This study aims to develop a two-stage, fully automated sperm morphology classification framework that can accurately identify a wide spectrum of abnormalities. The framework is designed to reduce subjectivity, minimize misclassification between visually similar categories, and provide more reliable diagnostic support in reproductive healthcare. Methods: A novel two-stage deep learning-based framework is proposed utilizing images from three staining-specific versions of a comprehensive 18-class dataset. In the first stage, sperm images are categorized into two principal groups: (1) head and neck region abnormalities, and (2) normal morphology together with tail-related abnormalities. In the second stage, a customized ensemble model\u2014integrating four distinct deep learning architectures, including DeepMind\u2019s NFNet-F4 and vision transformer (ViT) variants\u2014is employed for detailed abnormality classification. Unlike conventional majority voting, a structured multi-stage voting strategy is introduced to enhance decision reliability. Results: The proposed framework consistently outperforms single-model baselines, achieving accuracies of 69.43%, 71.34%, and 68.41% across the three staining protocols. These results correspond to a statistically significant 4.38% improvement over prior approaches in the literature. Moreover, the two-stage system substantially reduces misclassification among visually similar categories, demonstrating enhanced ability to detect subtle morphological variations. Conclusions: The proposed two-stage, ensemble-based framework provides a robust and accurate solution for automated sperm morphology classification. By combining hierarchical classification with structured decision fusion, the method advances beyond traditional and single-model approaches, offering a reliable and scalable tool for clinical decision-making in male fertility assessment.<\/jats:p>","DOI":"10.3390\/diagnostics15172234","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T12:07:32Z","timestamp":1756901252000},"page":"2234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Category-Aware Two-Stage Divide-and-Ensemble Framework for Sperm Morphology Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8383-8807","authenticated-orcid":false,"given":"Ayd\u0131n Ka\u011fan","family":"Turkoglu","sequence":"first","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\/0000-0003-4591-7368","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\/0000-0002-5189-3166","authenticated-orcid":false,"given":"Hakk\u0131","family":"Uzun","sequence":"additional","affiliation":[{"name":"Department of Urology, Faculty of Medicine, Recep Tayyip Erdo\u011fan University, 53000 Rize, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0746-7693","authenticated-orcid":false,"given":"Abdulsamet","family":"Aktas","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Marmara University, 34840 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8988-4321","authenticated-orcid":false,"given":"Merve Huner","family":"Yigit","sequence":"additional","affiliation":[{"name":"Department of Medical Biochemistry, Faculty of Medicine, Recep Tayyip Erdo\u011fan University, 53000 Rize, 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"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). 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