[论文解读] A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

Asif H. Chowdhury, Md Fahim Ul Islam+4 more|arXiv (Cornell University)|Feb 19, 2026

COVID-19 diagnosis using AI被引用 0

一句话总结

该论文提出一种混合联邦学习集成，将基于CNN的迁移学习模型（VGG-19、Inception V3、DenseNet201）与 SWIN Transformer 结合，以诊断 COVID-19、肺炎和正常胸部X线，并在联邦环境中进行评估。

ABSTRACT

The significant advancements in computational power cre- ate a vast opportunity for using Artificial Intelligence in different ap- plications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medi- cal specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available tech- nology offered by Tensorflow and Keras along with Microsoft-developed Vision Transformer, that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hy- brid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information.

研究动机与目标

• 开发一种将迁移学习CNN与SWIN Transformer结合的融合模型用于肺部疾病检测。
• 利用联邦学习提升数据隐私、降低延迟，并在多家医院中提升模型鲁棒性。
• 在COVID-19与肺炎X线数据集上评估性能，并与单独模型进行对比。

提出的方法

• 在X线数据上分别训练基于CNN的模型（VGG-19、Inception V3、DenseNet201）与SWIN Transformer。
• 将CNN模型进行集成并与SWIN Transformer进行融合，构建混合融合模型。
• 应用联邦学习在多家医院分布式训练，聚合本地更新以更新全局模型。
• 通过训练/测试时间和准确率衡量性能；分析ROC-AUC和混淨矩阵。
• 将融合模型与单独模型对比，并报告总和融合与平均融合策略的结果。

实验结果

研究问题

• RQ1 混合CNN迁移学习模型与SWIN Transformer的融合是否在诊断准确性上优于单独模型？
• RQ2 与非联邦基线相比，联邦混合模型在 ROC-AUC 与混淨矩阵指标上有何提升？
• RQ3 在多医院场景中，联邦学习如何影响训练效率与资源使用？

主要发现

• 融合模型在测试数据集上的准确率为96.24%（总和融合），平均融合为94% 。
• 单独模型的准确率为4: VGG-19 94.4%、Inception V3 94.5%、DenseNet201 94.1%、SWIN Transformer 82.5%。
• SWIN Transformer 单独表现劣于CNN模型，准确率为82.5%。
• 联邦设置消耗较多硬件资源（如单次运行需约35 GB RAM）。
• 融合模型在ROC-AUC上高于单独模型，融合集成在所给图示中显示出更高的AUC。
• 不同模型的训练时间存在差异，总和融合模型报告的准确率最高。