Integrating lightweight convolutional neural network with entropy-informed channel attention and adaptive spatial attention for OCT-based retinal disease classification

Md. Rayhan Ahmed, Mohamed S. Shehata, Patricia Lasserre

Abstract: Highlights•Context-guided CNN for retinal disease classification with 2.32M parameters.•EICA boosts classification by prioritizing high-entropy channels to capture key features.•Multi-kernel Adaptive Spatial Attention and DsConv capture multi-scale features.•XAI methods like Grad-CAM and LIME provide visual interpretation of decision-making.•Obtains an average accuracy of 96.46% across six benchmark OCT B-scan datasets.