A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm
Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require sel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6613 |
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| author | Muhammad Izzuddin Mahali Jenq-Shiou Leu Jeremie Theddy Darmawan Cries Avian Nabil Bachroin Setya Widyawan Prakosa Muhamad Faisal Nur Achmad Sulistyo Putro |
| author_facet | Muhammad Izzuddin Mahali Jenq-Shiou Leu Jeremie Theddy Darmawan Cries Avian Nabil Bachroin Setya Widyawan Prakosa Muhamad Faisal Nur Achmad Sulistyo Putro |
| author_sort | Muhammad Izzuddin Mahali |
| collection | DOAJ |
| description | Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require selecting healthy sperm. Hence, machine learning algorithms are presented as the subject of this research to effectively modernize and make accurate standards and decisions in classifying sperm. In this study, we developed a deep learning fusion architecture called SwinMobile that combines the Shifted Windows Vision Transformer (Swin) and MobileNetV3 into a unified feature space and classifies sperm from impurities in the SVIA Subset-C. Swin Transformer provides long-range feature extraction, while MobileNetV3 is responsible for extracting local features. We also explored incorporating an autoencoder into the architecture for an automatic noise-removing model. Our model was tested on SVIA, HuSHem, and SMIDS. Comparison to the state-of-the-art models was based on F1-score and accuracy. Our deep learning results accurately classified sperm and performed well in direct comparisons with previous approaches despite the datasets’ different characteristics. We compared the model from Xception on the SVIA dataset, the MC-HSH model on the HuSHem dataset, and Ilhan et al.’s model on the SMIDS dataset and the astonishing results given by our model. The proposed model, especially SwinMobile-AE, has strong classification capabilities that enable it to function with high classification results on three different datasets. We propose that our deep learning approach to sperm classification is suitable for modernizing the clinical world. Our work leverages the potential of artificial intelligence technologies to rival humans in terms of accuracy, reliability, and speed of analysis. The SwinMobile-AE method we provide can achieve better results than state-of-the-art, even for three different datasets. Our results were benchmarked by comparisons with three datasets, which included SVIA, HuSHem, and SMIDS, respectively (95.4% vs. 94.9%), (97.6% vs. 95.7%), and (91.7% vs. 90.9%). Thus, the proposed model can realize technological advances in classifying sperm morphology based on the evidential results with three different datasets, each having its characteristics related to data size, number of classes, and color space. |
| first_indexed | 2024-03-11T00:40:05Z |
| format | Article |
| id | doaj.art-6ea0b16685b14ea9bebc307014dc46f1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:05Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6ea0b16685b14ea9bebc307014dc46f12023-11-18T21:20:10ZengMDPI AGSensors1424-82202023-07-012314661310.3390/s23146613A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human SpermMuhammad Izzuddin Mahali0Jenq-Shiou Leu1Jeremie Theddy Darmawan2Cries Avian3Nabil Bachroin4Setya Widyawan Prakosa5Muhamad Faisal6Nur Achmad Sulistyo Putro7Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartement of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, TaiwanInfertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require selecting healthy sperm. Hence, machine learning algorithms are presented as the subject of this research to effectively modernize and make accurate standards and decisions in classifying sperm. In this study, we developed a deep learning fusion architecture called SwinMobile that combines the Shifted Windows Vision Transformer (Swin) and MobileNetV3 into a unified feature space and classifies sperm from impurities in the SVIA Subset-C. Swin Transformer provides long-range feature extraction, while MobileNetV3 is responsible for extracting local features. We also explored incorporating an autoencoder into the architecture for an automatic noise-removing model. Our model was tested on SVIA, HuSHem, and SMIDS. Comparison to the state-of-the-art models was based on F1-score and accuracy. Our deep learning results accurately classified sperm and performed well in direct comparisons with previous approaches despite the datasets’ different characteristics. We compared the model from Xception on the SVIA dataset, the MC-HSH model on the HuSHem dataset, and Ilhan et al.’s model on the SMIDS dataset and the astonishing results given by our model. The proposed model, especially SwinMobile-AE, has strong classification capabilities that enable it to function with high classification results on three different datasets. We propose that our deep learning approach to sperm classification is suitable for modernizing the clinical world. Our work leverages the potential of artificial intelligence technologies to rival humans in terms of accuracy, reliability, and speed of analysis. The SwinMobile-AE method we provide can achieve better results than state-of-the-art, even for three different datasets. Our results were benchmarked by comparisons with three datasets, which included SVIA, HuSHem, and SMIDS, respectively (95.4% vs. 94.9%), (97.6% vs. 95.7%), and (91.7% vs. 90.9%). Thus, the proposed model can realize technological advances in classifying sperm morphology based on the evidential results with three different datasets, each having its characteristics related to data size, number of classes, and color space.https://www.mdpi.com/1424-8220/23/14/6613deep learningdual architecture fusionmorphological classificationspermswin transformer |
| spellingShingle | Muhammad Izzuddin Mahali Jenq-Shiou Leu Jeremie Theddy Darmawan Cries Avian Nabil Bachroin Setya Widyawan Prakosa Muhamad Faisal Nur Achmad Sulistyo Putro A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm Sensors deep learning dual architecture fusion morphological classification sperm swin transformer |
| title | A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm |
| title_full | A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm |
| title_fullStr | A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm |
| title_full_unstemmed | A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm |
| title_short | A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm |
| title_sort | dual architecture fusion and autoencoder for automatic morphological classification of human sperm |
| topic | deep learning dual architecture fusion morphological classification sperm swin transformer |
| url | https://www.mdpi.com/1424-8220/23/14/6613 |
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