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To propose a deep learning-based retinal image quality classification network, FA-Net, to make convolutional neural network (CNN) more suitable for image quality assessment in eye disease screening system.
The main network of FA-Net was composed of VGG-19.On this basis, attention mechanism was added to the CNN.By using transfer learning method in training, the weight of ImageNet was used to initialize the network.The attention net is based on foreground extraction by extracting the blood vessel and suspected regions of lesion and assigning higher weights to region of interest to enhance the learning of these important areas.
Total of 2 894 fundus images were used for training FA-Net.FA-Net achieved 97.65% classification accuracy on a test set containing 2 170 fundus images, with the sensitivity and specificity of 0.978 and 0.960, respectively, and the area under curve(AUC) was 0.995.
Compared with other CNNs, the proposed FA-Net has better classification performance and can evaluate retinal fundus image quality more accurately and efficiently.The network takes into account the human visual system (HVS) and human attention mechanism.By adding attention module into the VGG-19 network structure, the classification results can be better interpreted as well as better classification performance.