Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.

•A novel weighted path convolutional neural network (CNN) architecture, called WP-CNN, is proposed to classify the diabetic retinopathy and achieves an accuracy of 94.23% with sensitivity of 90.94%, specificity of 95.74%, an area under the receiver operating curve of 0.9823 and F1-score of 0.9087.•The WP-CNN can be built by stacking weighted path blocks. The output of the weighted block can obtain the accurate diagnosis feature and reducing the multipath feature redundancy.•Comparing with the state-of-art CNN architectures, WP-CNN can be trained faster and obtain the better classification performance with only one third of convolution layers number.