Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis
Sperm morphology is crucial in semen analysis for diagnosing male infertility. To reduce limitations in visual assessment, such as variability in biological conditions and the biologist’s experience, developing computer-based sperm analysis techniques is imperative. In this study, a total...
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2024-01-01
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| author | Abdulsamet Aktas Gorkem Serbes Merve Huner Yigit Nizamettin Aydin Hakki Uzun Hamza Osman Ilhan |
| author_facet | Abdulsamet Aktas Gorkem Serbes Merve Huner Yigit Nizamettin Aydin Hakki Uzun Hamza Osman Ilhan |
| author_sort | Abdulsamet Aktas |
| collection | DOAJ |
| description | Sperm morphology is crucial in semen analysis for diagnosing male infertility. To reduce limitations in visual assessment, such as variability in biological conditions and the biologist’s experience, developing computer-based sperm analysis techniques is imperative. In this study, a total of 49345 RGB sperm morphology patches were obtained using the proposed image acquisition technique and three different Diff-Quick staining methods: BesLab, Histoplus, and GBL. The images were labeled by experts under 18 classes, including sperm head, neck, and tail abnormality types, along with a normal class. The head category includes amorphous, tapered, double, pyriform, pin, vacuolated, narrow acrosome, and round. The neck category encompasses thin, thick, twisted, and asymmetrical. The tail category includes double, curly, long, short, and twisted. The Efficient-V2-Medium achieved accuracy rates of 65.05% and 67.42% on the BesLab and Histoplus datasets, respectively, while the GBL dataset yielded an accuracy of 63.58% using the Efficient-V2-Small. This study experimentally demonstrates that the Histoplus staining method is more suitable for deep learning-based automated analysis systems. As a reference for future studies, 35 different deep learning architectures were trained on the proposed dataset, establishing a classification baseline. The results show that the dataset can be successfully applied to complex deep learning models. Additionally, it addresses the absence of a large-scale sperm morphology analysis public datasets and can serve as a standard benchmark for future studies. |
| format | Article |
| id | doaj-art-8251350310654dbd81278cbef09fa1a9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-8251350310654dbd81278cbef09fa1a92024-12-31T00:01:05ZengIEEEIEEE Access2169-35362024-01-011219607019609110.1109/ACCESS.2024.352164310812754Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology AnalysisAbdulsamet Aktas0https://orcid.org/0000-0003-0746-7693Gorkem Serbes1https://orcid.org/0000-0003-4591-7368Merve Huner Yigit2https://orcid.org/0000-0001-8988-4321Nizamettin Aydin3https://orcid.org/0000-0003-0022-2247Hakki Uzun4https://orcid.org/0000-0002-5189-3166Hamza Osman Ilhan5https://orcid.org/0000-0002-1753-2703Department of Computer Engineering, Marmara University, İstanbul, TürkiyeDepartment of Biomedical Engineering, Yildiz Technical University, İstanbul, TürkiyeDepartment of Biochemistry, Recep Tayyip Erdoğan University, Rize, TürkiyeDepartment of Computer Engineering, Istanbul Technical University, İstanbul, TürkiyeDepartment of Urology, Faculty of Medicine, Recep Tayyip Erdoğan University, Rize, TürkiyeDepartment of Computer Engineering, Yildiz Technical University, Ã2°stanbul, TürkiyeSperm morphology is crucial in semen analysis for diagnosing male infertility. To reduce limitations in visual assessment, such as variability in biological conditions and the biologist’s experience, developing computer-based sperm analysis techniques is imperative. In this study, a total of 49345 RGB sperm morphology patches were obtained using the proposed image acquisition technique and three different Diff-Quick staining methods: BesLab, Histoplus, and GBL. The images were labeled by experts under 18 classes, including sperm head, neck, and tail abnormality types, along with a normal class. The head category includes amorphous, tapered, double, pyriform, pin, vacuolated, narrow acrosome, and round. The neck category encompasses thin, thick, twisted, and asymmetrical. The tail category includes double, curly, long, short, and twisted. The Efficient-V2-Medium achieved accuracy rates of 65.05% and 67.42% on the BesLab and Histoplus datasets, respectively, while the GBL dataset yielded an accuracy of 63.58% using the Efficient-V2-Small. This study experimentally demonstrates that the Histoplus staining method is more suitable for deep learning-based automated analysis systems. As a reference for future studies, 35 different deep learning architectures were trained on the proposed dataset, establishing a classification baseline. The results show that the dataset can be successfully applied to complex deep learning models. Additionally, it addresses the absence of a large-scale sperm morphology analysis public datasets and can serve as a standard benchmark for future studies.https://ieeexplore.ieee.org/document/10812754/Dataset benchmarkdeep learningdiff-quick staining methodsinfertility diagnosissperm morphology analysistransformer |
| spellingShingle | Abdulsamet Aktas Gorkem Serbes Merve Huner Yigit Nizamettin Aydin Hakki Uzun Hamza Osman Ilhan Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis IEEE Access Dataset benchmark deep learning diff-quick staining methods infertility diagnosis sperm morphology analysis transformer |
| title | Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis |
| title_full | Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis |
| title_fullStr | Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis |
| title_full_unstemmed | Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis |
| title_short | Hi-LabSpermMorpho: A Novel Expert-Labeled Dataset With Extensive Abnormality Classes for Deep Learning-Based Sperm Morphology Analysis |
| title_sort | hi labspermmorpho a novel expert labeled dataset with extensive abnormality classes for deep learning based sperm morphology analysis |
| topic | Dataset benchmark deep learning diff-quick staining methods infertility diagnosis sperm morphology analysis transformer |
| url | https://ieeexplore.ieee.org/document/10812754/ |
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