A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images
<|reference_start|>A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images: Deep learning and computer vision methods are nowadays predominantly used in the field of ophthalmology. In this paper, we present an attention-aided DenseNet-121 for classifying normal and glaucomatous eyes from fundus images. It involves the convolutional block attention module to highlight relevant spatial and channel features extracted by DenseNet-121. The channel recalibration module further enriches the features by utilizing edge information along with the statistical features of the spatial dimension. For the experiments, two standard datasets, namely RIM-ONE and ACRIMA, have been used. Our method has shown superior results than state-of-the-art models. An ablation study has also been conducted to show the effectiveness of each of the components. The code of the proposed work is available at: https://github.com/Soham2004GitHub/DADGC.<|reference_end|>
@article{chakraborty2024a,
    title={A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from
  Fundus Images},
    author={Soham Chakraborty, Ayush Roy, Payel Pramanik, Daria Valenkova, Ram
  Sarkar},
    journal={arXiv preprint arXiv:2406.15113},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15113},
    primaryClass={eess.IV cs.AI cs.CV}
}