ARXIV:2602.17566 · MEDICAL AI · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

Develop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

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PROBLEM

Develop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the…

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainDevelop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Develop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

Develop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

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References(7)

COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer

2021Juntao Jiang, Shuyi Lin

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach

2020Sara Hosseinzadeh Kassani, P. H. Kassani et al.

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

2020Shervin Minaee, R. Kafieh et al.

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

2020E. E. Hemdan, M. Shouman et al.

Federated Learning: Challenges, Methods, and Future Directions

2019Tian Li, Anit Kumar Sahu et al.

Variational Federated Multi-Task Learning

2019Luca Corinzia, J. Buhmann

Federated Multi-Task Learning

2017Virginia Smith, Chao-Kai Chiang et al.

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Competitive landscape

Develop a federated learning-based ensemble model for accurate lung disease diagnosis with enhanced data security.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

References(7)

COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer

2021Juntao Jiang, Shuyi Lin

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach

2020Sara Hosseinzadeh Kassani, P. H. Kassani et al.

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

2020Shervin Minaee, R. Kafieh et al.

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

2020E. E. Hemdan, M. Shouman et al.

Federated Learning: Challenges, Methods, and Future Directions

2019Tian Li, Anit Kumar Sahu et al.

Variational Federated Multi-Task Learning

2019Luca Corinzia, J. Buhmann

Federated Multi-Task Learning

2017Virginia Smith, Chao-Kai Chiang et al.

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

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

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