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Retinopathy of prematurity (ROP) is the main cause of blindness in children worldwide. The severity of ROP can be reflected by staging, zoning and plus disease. Specially, some studies have shown that zone recognition is more important than staging. However, due to the subjective factors, ophthalmologists are often inconsistent in their recognition of zones according to fundus images. Therefore, automated zones recognition of ROP is particularly important. In this paper, we propose a new ROP zones recognition network, in which pre-trained DenseNet121 is taken as backbone and a proposed attention block named Spatial and Channel Attention Block (SACAB) and deep supervision strategy are introduced. Our main contributions are: (1) Demonstrating the 2D convolutional neural network model pre-trained on natural images can be fine-tuned for automated zones recognition of ROP. (2) Based on pre-trained DenseNet121, we propose two improved schemes which effectively integrate attention mechanism and deep supervision learning for ROP zoning. The proposed method was evaluated on 662 retinal fundus images (82 zone I, 299 zone II, 281 zone III) from 148 examinations with 5-fold cross validation strategy. The results show that the performance of the proposed ROP zone recognition network achieves 0.8852 for accuracy (ACC), 0.8850 for weighted F1 score (W_F1) and 0.8699 for kappa. The preliminary experimental results show the effectiveness of the proposed method.
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