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It is feasible to recognize the presence and seriousness of eye disease byinvestigating the progressions in retinal biological structure. Fundusexamination is a diagnostic procedure to examine the biological structure andanomaly of the eye. Ophthalmic diseases like glaucoma, diabetic retinopathy,and cataract are the main reason for visual impairment around the world. OcularDisease Intelligent Recognition (ODIR-5K) is a benchmark structured fundusimage dataset utilized by researchers for multi-label multi-diseaseclassification of fundus images. This work presents a discriminative kernelconvolution network (DKCNet), which explores discriminative region-wisefeatures without adding extra computational cost. DKCNet is composed of anattention block followed by a squeeze and excitation (SE) block. The attentionblock takes features from the backbone network and generates discriminativefeature attention maps. The SE block takes the discriminative feature maps andimproves channel interdependencies. Better performance of DKCNet is observedwith InceptionResnet backbone network for multi-label classification of ODIR-5Kfundus images with 96.08 AUC, 94.28 F1-score and 0.81 kappa score. The proposedmethod splits the common target label for an eye pair based on the diagnostickeyword. Based on these labels oversampling and undersampling is done toresolve class imbalance. To check the biasness of proposed model towardstraining data, the model trained on ODIR dataset is tested on three publiclyavailable benchmark datasets. It is found to give good performance oncompletely unseen fundus images also.

Discriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Dataset