ArticleIET systems biology2022
SRAS-net: Low-resolution chromosome image classification based on deep learning.
Article in IET systems biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- [Construction of a Multi-Factor Quantitative Scoring Model for Chromosome Karyotype Analysis].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Agro-Morphological and Cytogenetic Characterization ofPlants (Basel, Switzerland) · 2026Article
- Flemboda artificial intelligence: hybrid fuzzy-convolutional neural network for efficient chromosome abnormality classification.Molecular genetics and genomics : MGG · 2026Article
- An automatic progressive chromosome segmentation approach using deep learning with traditional image processing.Medical & biological engineering & computing · 2024Article
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
- An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification.Scientific data · 2023Article
- EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation.Frontiers in microbiology · 2023Article
- SRAS-net: Low-resolution chromosome image classification based on deep learning.IET systems biology · 2022Article
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4 authors.
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Abstract
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.
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