Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.
Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures t...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0246988&type=printable |
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| author | Mehran Ghafari Justin Clark Hao-Bo Guo Ruofan Yu Yu Sun Weiwei Dang Hong Qin |
| author_facet | Mehran Ghafari Justin Clark Hao-Bo Guo Ruofan Yu Yu Sun Weiwei Dang Hong Qin |
| author_sort | Mehran Ghafari |
| collection | DOAJ |
| description | Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging. |
| format | Article |
| id | doaj-art-9e47ef99de7948fa88063dbf9fcb778b |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-9e47ef99de7948fa88063dbf9fcb778b2025-08-20T02:00:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024698810.1371/journal.pone.0246988Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.Mehran GhafariJustin ClarkHao-Bo GuoRuofan YuYu SunWeiwei DangHong QinMicrofluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0246988&type=printable |
| spellingShingle | Mehran Ghafari Justin Clark Hao-Bo Guo Ruofan Yu Yu Sun Weiwei Dang Hong Qin Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. PLoS ONE |
| title | Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. |
| title_full | Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. |
| title_fullStr | Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. |
| title_full_unstemmed | Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. |
| title_short | Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. |
| title_sort | complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0246988&type=printable |
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