A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910497568194560 |
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| author | Chakraborty, Soham Roy, Ayush Pramanik, Payel Valenkova, Daria Sarkar, Ram |
| author_facet | Chakraborty, Soham Roy, Ayush Pramanik, Payel Valenkova, Daria Sarkar, Ram |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_15113 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images Chakraborty, Soham Roy, Ayush Pramanik, Payel Valenkova, Daria Sarkar, Ram Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.15113 |