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

TL;DR

This paper introduces DKCNet, a novel discriminative kernel convolution network that enhances multi-label ophthalmic disease detection on imbalanced fundus datasets by leveraging attention and SE blocks, achieving high accuracy and robustness.

Contribution

The work presents a new DKCNet architecture that effectively captures discriminative features without extra computational cost, improving multi-label classification on imbalanced fundus image datasets.

Findings

Achieved 96.08% AUC on ODIR-5K dataset.

Demonstrated robustness on unseen datasets.

Improved multi-label classification accuracy.

Abstract

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.