SLight-Net: a lightweight spectral-layer aware network for retinal disease detection based on optical coherence tomography.

Background: Retinal diseases are a major cause of preventable visual impairment, and optical coherence tomography (OCT) provides high-resolution cross-sectional imaging for retinal assessment. However, reliable interpretation of OCT B-scans remains challenging because of speckle noise, subtle layer-wise changes, and visually similar pathological patterns. Methods: We propose SLight-Net, a lightweight spectral-layer aware network for retinal OCT classification. The model is built on a compact three-stage convolutional backbone and incorporates two OCT-specific modules. The Frequency-Aware Spectral-Spatial Encoder (FASE) integrates local convolution, dilated contextual modeling, and learnable spectral modulation to capture multi-scale structural and frequency-aware retinal features. The Retinal Layer Depth Attention (RLDA) module further introduces a depth-direction anatomical prior to recalibrate feature responses along retinal layers. Deep supervision and exponential moving-average weight updating are used to improve optimization stability. Results: On the OCT-C8 benchmark, SLight-Net achieves 98.21% classification accuracy with only 1.204M parameters. Additional evaluation on OCT2017 shows 99.30% accuracy, suggesting that the model maintains stable performance under a different class setting while remaining compact. Conclusion: These findings indicate that OCT-specific spectral and layer-aware priors can support efficient retinal disease classification without relying on large generic backbones, providing a practical basis for lightweight computer-aided OCT analysis.