An Imbalanced Image Classification Method for the Cell Cycle Phase
The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature e...
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MDPI AG
2021-06-01
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| Online Access: | https://www.mdpi.com/2078-2489/12/6/249 |
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| author | Xin Jin Yuanwen Zou Zhongbing Huang |
| author_facet | Xin Jin Yuanwen Zou Zhongbing Huang |
| author_sort | Xin Jin |
| collection | DOAJ |
| description | The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images. |
| first_indexed | 2024-03-10T10:24:01Z |
| format | Article |
| id | doaj.art-02f718f4e5cf41ffaafb8b704bcdac38 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T10:24:01Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-02f718f4e5cf41ffaafb8b704bcdac382023-11-22T00:08:38ZengMDPI AGInformation2078-24892021-06-0112624910.3390/info12060249An Imbalanced Image Classification Method for the Cell Cycle PhaseXin Jin0Yuanwen Zou1Zhongbing Huang2College of Biomedical Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Biomedical Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Biomedical Engineering, Sichuan University, Chengdu 610065, ChinaThe cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.https://www.mdpi.com/2078-2489/12/6/249cell cycleimage classificationimbalanced image datasetsdeep learningWasserstein generative adversarial network-gradient penaltyresidual network |
| spellingShingle | Xin Jin Yuanwen Zou Zhongbing Huang An Imbalanced Image Classification Method for the Cell Cycle Phase Information cell cycle image classification imbalanced image datasets deep learning Wasserstein generative adversarial network-gradient penalty residual network |
| title | An Imbalanced Image Classification Method for the Cell Cycle Phase |
| title_full | An Imbalanced Image Classification Method for the Cell Cycle Phase |
| title_fullStr | An Imbalanced Image Classification Method for the Cell Cycle Phase |
| title_full_unstemmed | An Imbalanced Image Classification Method for the Cell Cycle Phase |
| title_short | An Imbalanced Image Classification Method for the Cell Cycle Phase |
| title_sort | imbalanced image classification method for the cell cycle phase |
| topic | cell cycle image classification imbalanced image datasets deep learning Wasserstein generative adversarial network-gradient penalty residual network |
| url | https://www.mdpi.com/2078-2489/12/6/249 |
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