用博弈积分融合多个模型特征，提升肺部X光片判别新冠的准确率。
A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images using Choquet Integral and Differential Evolution Optimization
- 通过博弈积分聚合多个预训练模型的深层特征，捕捉模型间非线性交互。
- 在COVIDx数据集上三分类准确率达98%，二分类达99.50%，优于单个模型。
- 适合医学影像诊断、深度学习集成学习研究者参考。
新冠疫情全球肆虐，严重冲击公共卫生体系。尽管RT-PCR是诊断黄金标准，但存在假阴性风险。本文提出一种基于预训练深度卷积神经网络（DCNN）的集成学习诊断系统，利用胸部X光（CXR）图像识别新冠感染。通过Choquet积分融合各模型最终隐藏层的特征向量，捕捉传统线性方法无法表达的模型间交互关系，并结合Sugeno-λ测度理论构建模糊测度，使用差分进化算法优化模糊密度。开发了基于TensorFlow的Choquet运算层以实现高效特征聚合。在COVIDx数据集上的实验表明，该集成模型三分类准确率为98%，二分类准确率达99.50%，优于DenseNet-201（三分类97%、二分类98.75%）、Inception-v3（三分类96.25%、二分类98.50%）和Xception（三分类94.50%、二分类98%），并超越多数已有方法。
原文摘要 · Abstract (English)
The COVID-19 pandemic has profoundly impacted billions globally. It challenges public health and healthcare systems due to its rapid spread and severe respiratory effects. An effective strategy to mitigate the COVID-19 pandemic involves integrating testing to identify infected individuals. While RT-PCR is considered the gold standard for diagnosing COVID-19, it has some limitations such as the risk of false negatives. To address this problem, this paper introduces a novel Deep Learning Diagnosis System that integrates pre-trained Deep Convolutional Neural Networks (DCNNs) within an ensemble learning framework to achieve precise identification of COVID-19 cases from Chest X-ray (CXR) images. We combine feature vectors from the final hidden layers of pre-trained DCNNs using the Choquet integral to capture interactions between different DCNNs that a linear approach cannot. We employed Sugeno-$λ$ measure theory to derive fuzzy measures for subsets of networks to enable aggregation. We utilized Differential Evolution to estimate fuzzy densities. We developed a TensorFlow-based layer for Choquet operation to facilitate efficient aggregation, due to the intricacies involved in aggregating feature vectors. Experimental results on the COVIDx dataset show that our ensemble model achieved 98\% accuracy in three-class classification and 99.50\% in binary classification, outperforming its components-DenseNet-201 (97\% for three-class, 98.75\% for binary), Inception-v3 (96.25\% for three-class, 98.50\% for binary), and Xception (94.50\% for three-class, 98\% for binary)-and surpassing many previous methods.
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