融合Transformer与CNN的联邦学习模型，提升肺病诊断准确率
A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN
- 用SWIN Transformer和CNN构建混合模型，结合联邦学习实现分布式训练
- 在真实数据上实现新冠与肺炎检测，支持实时持续学习提升诊断能力
- 适合医疗AI研究者及需要隐私保护诊断系统的医院团队参考
计算能力的显著提升为人工智能在医疗健康领域的应用创造了巨大机遇。本文提出一种基于联邦学习的混合集成方法，融合SWIN Transformer与CNN模型，用于肺部疾病诊断。该方法利用联邦学习实现医疗数据的安全分布式处理，避免数据集中风险。通过整合DenseNet201、Inception V3、VGG19等先进CNN模型与微软开发的Vision Transformer，构建高效可靠的混合模型，支持对新冠与肺炎的影像诊断。研究重点探讨了基于联邦学习的混合模型如何通过实时持续学习提高诊断准确率与病情严重程度预测能力，并确保模型安全与信息真实性。
原文摘要 · Abstract (English)
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.
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