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

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.

💡 Research Summary

The paper proposes a hybrid artificial‑intelligence system for diagnosing lung diseases—specifically COVID‑19, pneumonia, and normal conditions—from chest X‑ray images. The core idea is to combine three well‑known convolutional neural networks (CNNs)—VGG‑19, Inception‑V3, and DenseNet‑201—with a state‑of‑the‑art vision transformer, the Swin Transformer, and to train this ensemble under a federated learning (FL) framework that preserves patient privacy.

Model Architecture
Each of the three CNNs is first fine‑tuned on the collected X‑ray dataset using transfer learning. Their individual predictions are then merged through an ensemble strategy (the paper does not specify whether it is majority voting, averaging of probabilities, or a learned meta‑classifier). In parallel, a Swin Transformer is trained on the same data; this model processes the image as a hierarchy of shifted windows, enabling efficient global context capture even at high resolution. After both stages are trained, the authors concatenate the feature representations (or predictions) from the CNN ensemble and the Swin Transformer to form a single hybrid model, which is again fine‑tuned on the full dataset.

Federated Learning Setup
The authors envision a network of hospitals that each retain their own raw X‑ray images. Each site trains the local CNNs and the Swin Transformer on its private data, then uploads only the model weights to a central server. The server aggregates the received weights by selecting the top 80 % of models based on validation accuracy and merging them (the exact aggregation rule is not detailed). The resulting global model is redistributed to the hospitals for further local fine‑tuning, creating an iterative loop. This design is intended to protect data confidentiality while still benefiting from collective learning.

Data Collection and Pre‑processing
The dataset comprises publicly available COVID‑19, pneumonia, and normal X‑ray images, supplemented by a small number of hospital‑provided scans. The authors report removing corrupted files, discarding CT images mistakenly mixed with X‑rays, and applying standard augmentations (rotation, horizontal flip, zoom, scaling). An 80/20 train‑test split is used. However, precise numbers of images per class, sources, and labeling procedures are not disclosed.

Experimental Results
The manuscript lacks a thorough quantitative evaluation. No tables or figures present accuracy, sensitivity, specificity, area‑under‑the‑curve (AUC), or confusion matrices for the individual CNNs, the Swin Transformer, the ensemble, or the federated version. Consequently, the claimed performance improvements remain unsubstantiated.

Critical Assessment

1. Novelty – Combining CNNs with a Swin Transformer is conceptually reasonable, but similar hybrid approaches have appeared in recent literature. The novelty lies mainly in the packaging of these models within an FL pipeline, which itself is a well‑studied paradigm for medical imaging.
2. Methodological Gaps – The paper does not describe the ensemble method, the FL aggregation algorithm, communication cost analysis, or how data heterogeneity across hospitals is handled. The “top‑80 % model selection” rule is arbitrary and unsupported by experiments.
3. Reproducibility – Missing details about dataset size, class distribution, hyper‑parameters (learning rates, batch sizes, number of FL rounds), and hardware impede replication.
4. Evaluation Deficiency – Without baseline comparisons (e.g., single CNN, single Transformer, or existing COVID‑19 detection models) the reader cannot gauge the true benefit of the hybrid‑FL approach.
5. Security and Privacy – The authors claim privacy preservation but do not implement or discuss differential privacy, secure aggregation, or resistance to model inversion attacks, which are critical in real‑world deployments.

Conclusion and Recommendations
The study presents an appealing high‑level architecture that merges powerful vision models and leverages federated learning to address privacy concerns in medical imaging. However, the lack of concrete experimental evidence, insufficient methodological transparency, and limited discussion of FL challenges diminish its scientific impact. Future work should (a) release the exact dataset composition or use a benchmark like COVIDx, (b) provide rigorous performance metrics against strong baselines, (c) detail the FL protocol (communication rounds, aggregation rule, handling of non‑IID data), and (d) incorporate proven privacy‑enhancing techniques. Only with such thorough validation can the proposed hybrid FL ensemble be considered a viable tool for clinical lung disease diagnosis.