Multi-classification of fundus diseases based on DSRA-CNN

There are many kinds of fundus diseases, and early diagnosis is the key to prevent severe visual impairment. In this paper, we propose a deep learning model to automatically detect multiple fundus diseases which includes three parts. The first part is the pre-processing of the data including data screening, black border cropping, data augmentation, and normalization. The second part is a feature extraction network named DSRA-CNN based on Xception architecture, which combines the function blocks of DS block, DSR block, and SE block, among which DS block is composed of depthwise separable convolutions to reduce the amount of calculation, DSR block is composed of DS blocks and residual connections to strengthen the utilization of image features, SE block is used to screen the characteristic information. Lastly, based on the extracted features, we design a classifier to realize the classification of eight different fundus diseases. Experiments are performed on the ODIR dataset and the results of the accuracy, precision, F1 value, and kappa score of the DSRA-CNN network proposed in this paper are respectively 87.90%, 88.50%, 88.16%, and 86.17%. Compared with the original Xception network and existing advanced Convolutional Neural Networks(CNNs), our proposed network performs better.