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

Technical Deep Dive: Hybrid Federated Learning for Lung Disease Diagnosis

Overview

The paper presents a hybrid approach for lung disease diagnosis using deep learning, with a focus on COVID-19 and pneumonia detection. The key innovations are:

1. A fusion model that combines multiple transfer learning-based CNN models (VGG-19, Inception-V3, DenseNet201) with the SWIN Transformer model.
2. Integration of federated learning to enable secure, distributed model training across hospitals while preserving patient data privacy.

Methodology

1. Dataset: The authors collected a dataset of chest X-ray images for COVID-19, pneumonia, and normal cases. They preprocessed the data by removing corrupted images, augmenting the dataset, and splitting into 80% training and 20% testing.
2. CNN Models: The authors trained and evaluated three state-of-the-art CNN models independently: VGG-19, Inception-V3, and DenseNet201. These models achieved 94.4%, 94.5%, and 94.1% validation accuracy respectively.
3. SWIN Transformer: The authors also trained a SWIN Transformer model, a hierarchical transformer architecture well-suited for computer vision tasks. This model achieved 82.5% validation accuracy.
4. Fusion Model: To combine the strengths of the CNN and transformer models, the authors created a hybrid "fusion" model by ensembling the outputs of the VGG-19, Inception-V3, DenseNet201, and SWIN Transformer models. This fusion model achieved 96.24% validation accuracy when using a sum ensemble, and 94% when using an average ensemble.
5. Federated Learning: To enable secure, distributed model training and deployment, the authors integrated a federated learning approach. A central server coordinates the model training, with each hospital training on its local data and sharing model updates back to the server. This helps preserve patient data privacy while jointly improving the global diagnosis model.

Results

• The fusion model significantly outperformed the individual CNN and transformer models, demonstrating the value of the ensemble approach.
• The federated learning setup enabled distributed training, but the authors noted high resource requirements (35GB RAM per run) as a limitation.
• Comparison of the ROC-AUC curves showed the fusion model had the best overall performance.

Limitations and Uncertainties

• The dataset size, while larger than some prior work, may still be limited, potentially leading to overfitting issues observed in the training/validation results.
• The federated learning setup had high resource requirements, which could hinder real-world deployment at scale.
• The paper does not provide details on how the authors handled disagreements or inconsistencies across the individual models within the fusion ensemble.

Future Work

• Explore techniques to address the federated learning resource challenges, such as model compression or lightweight architectures.
• Investigate methods to handle concept drift in the federated learning setting, as hospital data distributions may evolve over time.
• Expand the evaluation to include a broader set of lung diseases beyond just COVID-19 and pneumonia.
• Analyze the time and space complexity of the proposed algorithms to further optimize efficiency.

Sources:

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