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

Das, Dipankar; Nayak, Deepak Ranjan; Pachori, Ram Bilas

doi:10.1016/j.imavis.2024.105042

Image and Vision Computing

2024

DOI: 10.1016/j.imavis.2024.105042

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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

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Cited by 2 publications

References 27 publications

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AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation

Meng,

Liu,

Wang

2025

Biomedical Signal Processing and Control

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AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation

Meng,

Liu,

Wang

2025

Biomedical Signal Processing and Control

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Fundus-DANet: Dilated Convolution and Fusion Attention Mechanism for Multilabel Retinal Fundus Image Classification

Yan,

Yang,

Huang

2024

Applied Sciences

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The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological features from images of different lesions and the inability to resolve the issue of the same lesion, which presents significant feature variances due to grading disparities. This paper proposes a multi-disease recognition network model, Fundus-DANet, based on the dilated convolution. It has two sub-modules to address the aforementioned issues: the interclass learning module (ILM) and the dilated-convolution convolutional block attention module (DA-CBAM). The DA-CBAM uses a convolutional block attention module (CBAM) and dilated convolution to extract and merge multiscale information from images. The ILM uses the channel attention mechanism to map the features to lower dimensions, facilitating exploring latent relationships between various categories. The results demonstrate that this model outperforms previous models in classifying fundus multilocular lesions in the OIA-ODIR dataset with 93% accuracy.

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

Cited by 2 publications

References 27 publications

AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation

AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation

Fundus-DANet: Dilated Convolution and Fusion Attention Mechanism for Multilabel Retinal Fundus Image Classification

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