Ensembled Deep Learning for the Classification of Human Sperm Head Morphology
Infertility is a growing global health concern, with male factor infertility contributing to half of all cases. Semen analysis is crucial to infertility diagnostics. However, sperm morphology assessment, as a routine part of analysis, is still performed manually and is thus highly subjective. Here,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-10-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200111 |
| _version_ | 1828139644145893376 |
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| author | Lindsay Spencer Jared Fernando Farzan Akbaridoust Klaus Ackermann Reza Nosrati |
| author_facet | Lindsay Spencer Jared Fernando Farzan Akbaridoust Klaus Ackermann Reza Nosrati |
| author_sort | Lindsay Spencer |
| collection | DOAJ |
| description | Infertility is a growing global health concern, with male factor infertility contributing to half of all cases. Semen analysis is crucial to infertility diagnostics. However, sperm morphology assessment, as a routine part of analysis, is still performed manually and is thus highly subjective. Here, a stacked ensemble of convolutional neural networks (CNNs) is presented for automated classification of human sperm head morphology. By combining traditional CNN models with modern residual and densely connected architectures using a multi‐class meta‐classifier, classification rate improvements of 2.7% (to 98.2%) and 2.3% (to 63.3%) on the HuSHeM and SCIAN‐MorphoSpermGS (SCIAN) datasets, respectively, are achieved. This considerable improvement in prediction performance is achieved as the meta‐classifier improves upon the individual classification rates of the base models by ≈8.5%. The ensembled deep learning model is a powerful step toward an automated sperm morphology analysis, providing new opportunities to standardize clinical practice and reduce treatment costs to improve patient treatment. |
| first_indexed | 2024-04-11T18:55:40Z |
| format | Article |
| id | doaj.art-d12c4f555d65476a84a59f5fb8624bbc |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-11T18:55:40Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-d12c4f555d65476a84a59f5fb8624bbc2022-12-22T04:08:13ZengWileyAdvanced Intelligent Systems2640-45672022-10-01410n/an/a10.1002/aisy.202200111Ensembled Deep Learning for the Classification of Human Sperm Head MorphologyLindsay Spencer0Jared Fernando1Farzan Akbaridoust2Klaus Ackermann3Reza Nosrati4Department of Mechanical and Aerospace Engineering Monash University Clayton Victoria 3800 AustraliaMonash DeepNeuron Monash University Clayton Victoria 3800 AustraliaDepartment of Mechanical and Aerospace Engineering Monash University Clayton Victoria 3800 AustraliaSoDa Labs and Department of Econometrics and Business Statistics Monash Business School Monash University Clayton VIC 3800 AustraliaDepartment of Mechanical and Aerospace Engineering Monash University Clayton Victoria 3800 AustraliaInfertility is a growing global health concern, with male factor infertility contributing to half of all cases. Semen analysis is crucial to infertility diagnostics. However, sperm morphology assessment, as a routine part of analysis, is still performed manually and is thus highly subjective. Here, a stacked ensemble of convolutional neural networks (CNNs) is presented for automated classification of human sperm head morphology. By combining traditional CNN models with modern residual and densely connected architectures using a multi‐class meta‐classifier, classification rate improvements of 2.7% (to 98.2%) and 2.3% (to 63.3%) on the HuSHeM and SCIAN‐MorphoSpermGS (SCIAN) datasets, respectively, are achieved. This considerable improvement in prediction performance is achieved as the meta‐classifier improves upon the individual classification rates of the base models by ≈8.5%. The ensembled deep learning model is a powerful step toward an automated sperm morphology analysis, providing new opportunities to standardize clinical practice and reduce treatment costs to improve patient treatment.https://doi.org/10.1002/aisy.202200111computer visiondeep learningmeta learningsperm analysissperm morphology |
| spellingShingle | Lindsay Spencer Jared Fernando Farzan Akbaridoust Klaus Ackermann Reza Nosrati Ensembled Deep Learning for the Classification of Human Sperm Head Morphology Advanced Intelligent Systems computer vision deep learning meta learning sperm analysis sperm morphology |
| title | Ensembled Deep Learning for the Classification of Human Sperm Head Morphology |
| title_full | Ensembled Deep Learning for the Classification of Human Sperm Head Morphology |
| title_fullStr | Ensembled Deep Learning for the Classification of Human Sperm Head Morphology |
| title_full_unstemmed | Ensembled Deep Learning for the Classification of Human Sperm Head Morphology |
| title_short | Ensembled Deep Learning for the Classification of Human Sperm Head Morphology |
| title_sort | ensembled deep learning for the classification of human sperm head morphology |
| topic | computer vision deep learning meta learning sperm analysis sperm morphology |
| url | https://doi.org/10.1002/aisy.202200111 |
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