Chromosome cascaded classification framework integrating image texture enhancement and super-resolution

doi:10.11772/j.issn.1001-9081.2025050568

Abstract

Abstract:

Chromosome karyotype analysis is of great significance in prenatal screening and genetic disease diagnosis. However， existing chromosome classification models are generally limited by insufficient feature extraction capability， high sensitivity to image quality， and inadequate attention to local details， leading to low overall classification accuracy， particularly the frequent misidentification of short chromosomes. Therefore， a coarse-to-fine chromosome cascaded classification framework integrating image texture enhancement and super-resolution techniques was proposed. Firstly， chromosomes were coarsely classified based on the International System for human Cytogenetic Nomenclature （ISCN）， and they were divided into long chromosomes and short chromosomes groups to mitigate class imbalance and feature confusion. Secondly， for the long chromosome classification task， a feature enhancement module was added to optimize the classification model's ability to perceive details of long chromosomes. Thirdly， considering the characteristics of short chromosome images， the super-resolution technique was introduced to improve image quality and the model's perceptual capability. Experimental results on a private dataset showed that the proposed framework achieved an overall chromosome classification accuracy of 98.91% and an overall chromosome F1-score of 98.77%， with 99.01% for long chromosomes and 98.31% for short chromosomes. By adopting differentiated classification strategies and task-specific models， this cascaded chromosome classification framework significantly enhances both classification accuracy and model robustness.

Key words: chromosome cascaded classification, feature enhancement, texture enhancement module, image super-resolution, channel attention mechanism

摘要：

染色体核型分析在产前筛查和遗传疾病诊断中具有重要意义，但现有染色体分类模型普遍存在特征提取能力不足、图像质量敏感性强以及对局部细节感知不充分等问题，导致染色体整体分类准确率不高，尤其是短小染色体常常被错误识别。因此，提出一种融合图像纹理增强与超分辨率技术的染色体粗粒度-细粒度级联分类框架。首先，基于国际人类细胞遗传命名系统（ISCN）对染色体进行粗分类，将染色体划分为长染色体与短小染色体组，缓解类别不均衡与特征混淆问题；其次，针对长染色体分类任务，增加特征增强模块优化分类模型对长染色体的细节感知能力；随后，针对短小染色体图像的特点引入图像超分辨率技术，提升图像质量与模型感知能力。在私有数据集上的实验结果表明，所提框架的染色体整体分类准确率达到98.91%，整体F1分数达到98.77%，其中长染色体的F1分数为99.01%，短小染色体的F1分数为98.31%。该染色体级联分类框架针对不同分类任务采取差异化分类策略与分类模型，显著提升了染色体分类精度和模型鲁棒性。

关键词: 染色体级联分类, 特征增强, 纹理增强模块, 图像超分辨率, 通道注意力机制

CLC Number:

TP391.41

Wen PENG, Bokai ZHANG, Jinwei LIN. Chromosome cascaded classification framework integrating image texture enhancement and super-resolution[J]. Journal of Computer Applications, 2026, 46(5): 1647-1657.

彭文, 张博凯, 林金炜. 融合图像纹理增强与超分辨率的染色体级联分类框架[J]. 《计算机应用》唯一官方网站, 2026, 46(5): 1647-1657.

Figures/Tables 22

References 36

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分类任务	长染色体数	短小染色体数	总数
染色体粗分类	31 773	14 590	46 363
长染色体细分类	31 773		31 773
短小染色体细分类		45 931	45 931
染色体分类	31 773	14 590	46 363

分类任务	长染色体数	短小染色体数	总数
染色体粗分类	31 773	14 590	46 363
长染色体细分类	31 773		31 773
短小染色体细分类		45 931	45 931
染色体分类	31 773	14 590	46 363

方法	Acc	P	R	F₁
SwinTransformer-small	98.81	98.58	98.66	98.62
ResNet18	99.13	99.18	98.81	98.99
Inception-ResNetV2	99.24	99.45	98.80	99.12
SIATE-Net	99.33	99.31	99.09	99.20
ResNet50	99.57	99.59	99.41	99.50
EfficientNet-B4	100.00	100.00	100.00	100.00

方法	Acc	P	R	F₁
SwinTransformer-small	98.81	98.58	98.66	98.62
ResNet18	99.13	99.18	98.81	98.99
Inception-ResNetV2	99.24	99.45	98.80	99.12
SIATE-Net	99.33	99.31	99.09	99.20
ResNet50	99.57	99.59	99.41	99.50
EfficientNet-B4	100.00	100.00	100.00	100.00

方法	Acc	P	R	F₁
EfficientNet-B4	98.89	98.95	98.67	98.77
FEM-EfficientNet-B4	99.05	99.03	99.00	99.01