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 releva...

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Bibliographic Details
Main Authors: Chakraborty, Soham, Roy, Ayush, Pramanik, Payel, Valenkova, Daria, Sarkar, Ram
Format: Conference Proceeding
Language:English
Subjects:
Channel recalibration
Computational modeling
Computer vision
Convolutional codes
Deep learning
Dual attention
Embedded computing
Fundus image
Glaucoma
Glaucoma detection
Image edge detection
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Summary: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.
ISSN:2637-9511
DOI:10.1109/MECO62516.2024.10577902