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SRAS‐net: Low‐resolution chromosome image classification based on deep learning
Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved go...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290780/ https://www.ncbi.nlm.nih.gov/pubmed/35373918 http://dx.doi.org/10.1049/syb2.12042 |
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author | Liu, Xiangbin Fu, Lijun Chun‐Wei Lin, Jerry Liu, Shuai |
author_facet | Liu, Xiangbin Fu, Lijun Chun‐Wei Lin, Jerry Liu, Shuai |
author_sort | Liu, Xiangbin |
collection | PubMed |
description | Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super‐resolution network, Self‐Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS‐net. The method first inputs the low‐resolution chromosome images into the super‐resolution network to generate high‐resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state‐of‐the‐art methods. |
format | Online Article Text |
id | pubmed-9290780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92907802022-07-20 SRAS‐net: Low‐resolution chromosome image classification based on deep learning Liu, Xiangbin Fu, Lijun Chun‐Wei Lin, Jerry Liu, Shuai IET Syst Biol Original Research Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super‐resolution network, Self‐Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS‐net. The method first inputs the low‐resolution chromosome images into the super‐resolution network to generate high‐resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state‐of‐the‐art methods. John Wiley and Sons Inc. 2022-04-04 /pmc/articles/PMC9290780/ /pubmed/35373918 http://dx.doi.org/10.1049/syb2.12042 Text en © 2022 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Liu, Xiangbin Fu, Lijun Chun‐Wei Lin, Jerry Liu, Shuai SRAS‐net: Low‐resolution chromosome image classification based on deep learning |
title | SRAS‐net: Low‐resolution chromosome image classification based on deep learning |
title_full | SRAS‐net: Low‐resolution chromosome image classification based on deep learning |
title_fullStr | SRAS‐net: Low‐resolution chromosome image classification based on deep learning |
title_full_unstemmed | SRAS‐net: Low‐resolution chromosome image classification based on deep learning |
title_short | SRAS‐net: Low‐resolution chromosome image classification based on deep learning |
title_sort | sras‐net: low‐resolution chromosome image classification based on deep learning |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290780/ https://www.ncbi.nlm.nih.gov/pubmed/35373918 http://dx.doi.org/10.1049/syb2.12042 |
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