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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968698/ https://www.ncbi.nlm.nih.gov/pubmed/33730031 http://dx.doi.org/10.1371/journal.pone.0246988 |
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author | Ghafari, Mehran Clark, Justin Guo, Hao-Bo Yu, Ruofan Sun, Yu Dang, Weiwei Qin, Hong |
author_facet | Ghafari, Mehran Clark, Justin Guo, Hao-Bo Yu, Ruofan Sun, Yu Dang, Weiwei Qin, Hong |
author_sort | Ghafari, Mehran |
collection | PubMed |
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 | Online Article Text |
id | pubmed-7968698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79686982021-03-31 Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells Ghafari, Mehran Clark, Justin Guo, Hao-Bo Yu, Ruofan Sun, Yu Dang, Weiwei Qin, Hong PLoS One Research Article 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. Public Library of Science 2021-03-17 /pmc/articles/PMC7968698/ /pubmed/33730031 http://dx.doi.org/10.1371/journal.pone.0246988 Text en © 2021 Ghafari et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ghafari, Mehran Clark, Justin Guo, Hao-Bo Yu, Ruofan Sun, Yu Dang, Weiwei Qin, Hong Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968698/ https://www.ncbi.nlm.nih.gov/pubmed/33730031 http://dx.doi.org/10.1371/journal.pone.0246988 |
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