A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

2026-02-19

AI Summary

This study addresses the challenge of balancing data privacy and model accuracy in intelligent diagnosis of pulmonary diseases such as COVID-19 and pneumonia. To this end, the authors propose a federated learning–based hybrid ensemble approach that, for the first time, integrates the SWIN Transformer with multiple prominent convolutional neural networks—DenseNet201, Inception V3, and VGG19—within a distributed framework. The proposed method not only preserves the privacy of sensitive medical data but also significantly enhances diagnostic accuracy while supporting continual learning. Implemented using TensorFlow/Keras, experimental results validate the dual advantages of the architecture in terms of both security and performance.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Federated Learning
• Lung Disease Diagnosis
• Medical Data Privacy
• COVID-19 Detection
• Pneumonia Classification

Innovation

• Federated Learning
• SWIN Transformer
• CNN Ensemble
• Lung Disease Diagnosis
• Privacy-Preserving AI