Automated Retinal Disease Classification from OCT Images Using a Lightweight CNN–SE Architecture
Abstract
Automated classification of retinal diseases from Optical Coherence Tomography (OCT) images can support timely and consistent identification of retinal abnormalities; however, many existing approaches rely on computationally intensive architectures or complex preprocessing procedures. This study proposes a lightweight Convolutional Neural Network with Squeeze-and-Excitation blocks (CNN–SE) for automated multi-class retinal disease classification while maintaining a compact computational structure. The proposed architecture consists of convolutional layers with SE-based channel recalibration, global average pooling, a fully connected layer, and dropout. Unlike approaches that depend on pretrained backbones or multi-stage segmentation, the proposed framework integrates channel-wise feature recalibration directly into a compact CNN, allowing the model to learn discriminative retinal representations with limited architectural complexity. Data augmentation was applied only to the training images, and the network was optimized using the Adam optimizer. The model was evaluated on the eight-class OCT-C8 dataset and the four-class OCT2017 dataset using accuracy, precision, recall, F1-score, and macro-ROC-AUC. The proposed model achieved 97.00% accuracy on OCT-C8 and 99.00% on OCT2017. To assess domain generalization, the model was trained exclusively on OCT-C8 and directly evaluated on the independent OCT2017 test set without retraining or fine-tuning. Across five random seeds, the cross-dataset evaluation achieved 98.74 ± 0.11% accuracy, with consistently high precision, recall, F1-score, and macro-ROC-AUC. The proposed network maintains a compact computational footprint and enables efficient image inference compared with the evaluated lightweight architectures. These results demonstrate that the proposed CNN–SE architecture provides strong classification performance and cross-dataset generalization while maintaining computational efficiency, making it a promising lightweight framework for automated retinal OCT image analysis
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