Hierarchical Pruning for Simplification of Convolutional Neural Networks in Diabetic Retinopathy Classification
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Convolutional neural networks (CNNs) are widely used in automatic detection and analysis of diabetic retinopathy (DR). Although CNNs have proper detection performance, their structural and computational complexity is troublesome. In this study, the problem of reducing CNN's structural complexity for DR analysis is addressed by proposing a hierarchical pruning method. The original VGG16-Net is modified to have fewer parameters and is employed for DR classification. To have an appropriate feature extraction, pre-trained model parameters on Image-Net dataset are used. Hierarchical pruning gradually eliminates the connections, filter channels, and filters to simplify the network structure. The proposed pruning method is evaluated using the Messidor image dataset which is a public dataset for DR classification. Simulation results show that by applying the proposed simplification method, 35% of the feature maps are pruned resulting in only 1.89% accuracy drop. This simplification could make CNN suitable for implementation inside medical diagnostic devices.
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