Fed-OCT-IoT: CBAM-Augmented EfficientNet with Federated Learning and Explainable AI for Retinal OCT Diagnosis

There are three primary obstacles to the automated
classification of retinal diseases using OCT: severe class imbalance, the high expense of transformer-based models, and
complications in gathering patient data from multiple medical
facilities. We address all three obstacles. We suggest utilizing an
EfficientNet-B3 backbone combined with a Convolutional Block
Attention Module (CBAM) for our framework to categorize seven
classes in the OCTDL dataset. To address the imbalance issue, we
employed a focal loss and oversampled certain data. The model
was assessed using a 5-fold cross-validation method at the patient
level along with a held-out test set, and was compared to four
independently trained baseline models. Our model outperformed
the baseline models in terms of accuracy. Component-wise
ablation studies revealed distinct contributions of the spatial and
channel-attention mechanisms. We also examined lighter versions
of federated learning, specifically FedAvg and FedProx for edge
devices in both IID and non-IID contexts. In this scenario,
FedProx did not perform better than FedAvg. We assessed
the model’s effectiveness using the Integrated Gradients and
Occlusion Sensitivity methods. Finally, we propose a theoretical
framework for IoT edge implementation and consider it viable
in terms of efficiency, without the need for physical testing in
person, which can be further investigated in future research.