Hybrid ensemble model that fuses the SWIN Transformer with state‑of‑the‑art CNNs (DenseNet201, Inception V3, VGG19).
Fully federated‑learning design enabling hospitals to share model updates without exchanging raw patient data.
Targeted detection of COVID‑19 and pneumonia from chest X‑ray images.
Implementation built on TensorFlow/Keras and Microsoft’s Vision Transformer technology.
The approach incorporates real‑time continual learning to adapt to new data and evolving disease patterns.
Authors claim enhanced diagnostic accuracy and improved system reliability through model security and authenticity.
📖 Full Retelling
The paper titled *A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN* was submitted on 19 February 2026 by authors Asif Hasan Chowdhury, Md. Fahim Islam, M Ragib Anjum Riad, Faiyaz Bin Hashem, Md. Tanzim Reza, and Md. Golam Rabiul Alam. It proposes a federated‑learning framework that combines the SWIN Transformer with several convolutional neural networks—DenseNet201, Inception V3, and VGG19—to detect COVID‑19 and pneumonia in chest X‑ray images, aiming to improve diagnostic accuracy while preserving patient data privacy across healthcare institutions.
🏷️ Themes
Artificial Intelligence in Healthcare, Federated Learning for Medical Data, Medical Image Analysis, COVID‑19 Diagnosis, Deep Learning Architectures
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Deep Analysis
Why It Matters
This research demonstrates how federated learning can combine powerful AI models like SWIN Transformer and CNNs to diagnose lung diseases while preserving patient privacy. By enabling hospitals to share insights without exchanging raw data, it could improve diagnostic accuracy and speed across diverse healthcare settings.
Context & Background
Federated learning allows decentralized model training on local data
SWIN Transformer is a vision transformer variant effective for image analysis
CNNs such as DenseNet201 and Inception V3 are established for medical imaging
What Happens Next
Future work may involve validating the model on larger, multi‑institution datasets and integrating it into clinical decision support tools. Regulatory approval and real‑world deployment will be necessary steps toward widespread adoption.
Frequently Asked Questions
What is federated learning?
Federated learning is a machine‑learning approach where models are trained across multiple devices or servers holding local data samples, without exchanging the data itself.
How does the hybrid model improve diagnosis?
By combining the feature extraction strengths of a SWIN Transformer with the classification power of CNNs, the hybrid model achieves higher accuracy than using either architecture alone.
Is the approach ready for clinical use?
The study is a pre‑print and has not yet undergone peer review or regulatory approval, so it is not yet ready for routine clinical deployment.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.17566 COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 19 Feb 2026] Title: A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN Authors: Asif Hasan Chowdhury , Md. Fahim Islam , M Ragib Anjum Riad , Faiyaz Bin Hashem , Md Tanzim Reza , Md. Golam Rabiul Alam View a PDF of the paper titled A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN, by Asif Hasan Chowdhury and 5 other authors View PDF HTML 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 ...
