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Attention-Based Densely Connected Convolutional Network for Chromosome Classification

Published: 13 July 2022 Publication History

Abstract

Karyotype analysis provides an important basis for diagnosis of genetic diseases. Currently, karyotyping requires considerable manual work and expertise experience, which is very time-consuming. Deep learning has been used in karyotype analysis in recent years. However, the specific information of chromosome length, centromere position, and length ratio in the image has not been paid extra attention in previous deep learning based karyotype analysis methods. In this study, we proposed an attention-based densely connected convolutional network (DenseNet) to automatically classify chromosomes. Specific image features has been paid attention and useless image features have been suppressed by adding channel attention to DenseNet. The accuracy of the proposed method achieved 93.12%, which is better than compared methods. The results show that the proposed chromosome classification method performs well in karyotype analysis, which has potential application in diagnosing genetic diseases caused by chromosome abnormalities.

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Cited By

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  • (2024)Enhanced label constrained contrastive learning for chromosome optical microscopic image classificationBiomedical Signal Processing and Control10.1016/j.bspc.2023.10582590(105825)Online publication date: Apr-2024
  • (2023)A Suitability Assessment Framework for Medical Cell Images in Chromosome AnalysisWeb Information Systems and Applications10.1007/978-981-99-6222-8_48(575-586)Online publication date: 9-Sep-2023

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cover image ACM Other conferences
ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence
March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 July 2022

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Author Tags

  1. Channel attention
  2. Chromosome classification
  3. DenseNet
  4. Karyotyping

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • National Nature Science Foundation of China
  • Jiangsu Innovation Team
  • Major Innovative Research Team of Suzhou
  • Suzhou Science & Technology Projects
  • Jinan Innovation Team, Quancheng 5150 Project
  • Shandong Natural Science Foundation

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ICCAI '22

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Cited By

View all
  • (2024)Enhanced label constrained contrastive learning for chromosome optical microscopic image classificationBiomedical Signal Processing and Control10.1016/j.bspc.2023.10582590(105825)Online publication date: Apr-2024
  • (2023)A Suitability Assessment Framework for Medical Cell Images in Chromosome AnalysisWeb Information Systems and Applications10.1007/978-981-99-6222-8_48(575-586)Online publication date: 9-Sep-2023

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