  It is feasible to recognize the presence and seriousness of eye disease by
investigating the progressions in retinal biological structure. Fundus
examination is a diagnostic procedure to examine the biological structure and
anomaly of the eye. Ophthalmic diseases like glaucoma, diabetic retinopathy,
and cataract are the main reason for visual impairment around the world. Ocular
Disease Intelligent Recognition (ODIR-5K) is a benchmark structured fundus
image dataset utilized by researchers for multi-label multi-disease
classification of fundus images. This work presents a discriminative kernel
convolution network (DKCNet), which explores discriminative region-wise
features without adding extra computational cost. DKCNet is composed of an
attention block followed by a squeeze and excitation (SE) block. The attention
block takes features from the backbone network and generates discriminative
feature attention maps. The SE block takes the discriminative feature maps and
improves channel interdependencies. Better performance of DKCNet is observed
with InceptionResnet backbone network for multi-label classification of ODIR-5K
fundus images with 96.08 AUC, 94.28 F1-score and 0.81 kappa score. The proposed
method splits the common target label for an eye pair based on the diagnostic
keyword. Based on these labels oversampling and undersampling is done to
resolve class imbalance. To check the biasness of proposed model towards
training data, the model trained on ODIR dataset is tested on three publicly
available benchmark datasets. It is found to give good performance on
completely unseen fundus images also.
