基于Stacking集成学习的CT图像质量分类

doi:10.12068/j.issn.1005-3026.2025.20240225

摘要/Abstract

摘要：

计算机断层扫描成像因低成本和高效性成为医学影像的一种重要形式，然而图像质量下降对诊断和预后造成严重干扰.针对单分类器性能有限，无法满足高精度CT（computed tomography）图像质量分类需求的问题，提出一种面向伪影识别的Stacking集成学习方法.基于分类多样性和各分类器性能考虑，选取具有异构性能的随机森林（random forest,RF)、反向传播神经网络（back propagation neural network，BPNN）和Inception v3作为基分类器，采用极限梯度提升(eXtreme gradient boosting，XGBoost)作为元学习器.实验结果表明，该方法准确率达到99.2%，使得模型的分类效果有所保证，能够满足不均衡数据集条件下CT图像质量分类的高准确率需求.

关键词: CT图像, 质量分类, Stacking集成学习, 不均衡数据集, 复杂网络

Abstract:

Computed tomography （CT） imaging has become an important form of medical imaging due to its low cost and high efficiency. However， the decline in image quality causes serious interference to diagnosis and prognosis. The limited performance of a single classifier cannot meet the requirements of high-precision CT image quality classification. To address this issue， a method based on Stacking ensemble learning was designed for artifact recognition. Based on classification diversity and individual classifier performance， random forest （RF）， back propagation neural network （BPNN）， and Inception v3， all of which are heterogeneous， were selected as the base classifiers. Extreme gradient boosting （XGBoost） was used as the meta-learner. The experimental results show that the accuracy of this method reaches 99.2%， which ensures the classification effect of the model and can meet the high accuracy requirements for CT image quality classification under the condition of an unbalanced dataset.

Key words: CT image, quality classification, Stacking ensemble learning, unbalanced dataset, complex network

中图分类号:

TP 393

刘怡文, 温涛, 毕远国, 朱宏博. 基于Stacking集成学习的CT图像质量分类[J]. 东北大学学报（自然科学版）, 2025, 46(11): 30-36.

Yi-wen LIU, Tao WEN, Yuan-guo BI, Hong-bo ZHU. CT Image Quality Classification Based on Stacking Ensemble Learning[J]. Journal of Northeastern University(Natural Science), 2025, 46(11): 30-36.

图/表 16

表 1 相关符号及其定义

Table 1 Related symbols and their definitions

符号	定义
G	无向图
V	图G中所有节点的集合
E	图G中所有边的集合
k_m	平均度
C	平均聚类系数

图1 典型的无伪影图像及有伪影图像（a）—无伪影CT图像；（b）—有伪影CT图像.

Fig.1 Typical examples of artifact-absent and artifact-present images

图2 无伪影图像及有伪影图像的拓扑结构（a）—无伪影CT图像；（b）—有伪影CT图像.

Fig.2 Topological structure of artifact-absent and artifact-present images

图3 Stacking两阶段模型示意图

Fig.3 Schematic diagram of two-phase Stacking model

表2 数据集信息

Table 2 Information of dataset

数据集	总样本	正例	负例
训练集	8 546	1 763	6 783
验证集	950	196	754
测试集	1 056	218	838

表3 分类器比较结果 (%)

Table 3 Comparison results of base classifier

模型	准确率	灵敏度	特异性
RF	93.2	85.8	95.1
XGBoost	94.1	88.5	95.6
BPNN	94.7	89.4	96.1
Inception v3	97.0	92.2	98.2

表4 基于Stacking集成学习的二分类实验结果 (Stacking ensemble learning %)

Table 4 Binary classification experiment results of

模型	准确率	灵敏度	特异性
Stacking集成模型	99.2	98.6	99.4
BPNN+Inception v3	98.1	95.9	98.7
Inception v3+RF+BPNN	98.8	97.2	99.2

图4 准确率比较的实验结果注：1—集成模型； 2—BPNN+Inception v3； 3—Inception v3+RF+BPNN； 4—RF； 5—XGBoost； 6—BPNN； 7—Inception v3，下同.

Fig.4 Experiment results of accuracy comparison

图5 灵敏度比较的实验结果

Fig.5 Experiment results of sensitivity comparison

图6 特异性比较的实验结果

Fig.6 Experiment results of specificity comparison

图7 可视化分类结果(有伪影图像)

Fig.7 Visualized classification results (artifact- present images)

图8 可视化分类结果(无伪影图像)

Fig.8 Visualized classification results (artifact-absent images)

图9 平均度箱线图

Fig.9 Boxplot of average degree

图10 边数箱线图

Fig.10 Boxplot of edge numbers

图11 平均聚类系数箱线图

Fig.11 Boxplot of average clustering coefficient

表5 基于Stacking集成学习的多分类实验结果 (Stacking ensemble learning %)

Table 5 Multi-classification experiment results of

伪影程度	准确率	灵敏度
严重伪影	95.5	94.8
轻微伪影	94.8	94.4
无伪影	96.8	96.3

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