  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.
