AES-Net: An adapter and enhanced self-attention guided network for multi-stage glaucoma classification using fundus images

Dipankar Das, Deepak Ranjan Nayak, Ram Bilas Pachori

Abstract: Highlights•A spatial adapter and enhanced self-attention based CNN, AES-Net, is proposed.•An enhanced self-attention module is proposed to learn class-specific features.•A comparative analysis with state-of-the-art attention mechanisms is performed.•Results on two datasets indicate the effectiveness of AES-Net over current methods.•The visualization results demonstrate the explainability and interpretability of the model.