Most of the eye disease classification methods are to classify a single category of diseases at different levels, and the network model has problems such as large parameters and complex calculations. To solve these problems, this paper proposed a lightweight fundus disease multi-classification network called MELCNet. The network used PPLCNet as the backbone, which was composed of input layer for feature extraction, parallel multi-scale structure, bilinear SE attention module, depth separable convolution and the h-swish activation function which calculated with smaller parameters, in order to pay attention to the key disease information of different scales and different diseases. Experimental results show that the proposed multi-scale attention lightweight network model has less parameters and computational complexity, and excellent classification results have been obtained in the multi-classification of the four selected fundus diseases and normal fundus images. The classification accuracy in the internal combined test set is 1.11% higher than that in ResNet-50, and the performance on the public datasets is improved by 2.5% relative to the multi-classification literature of similar type network by Xie et al, and compared with other lightweight classification networks, it has higher accuracy and strong robustness in the field of multi-classification of fundus diseases.

[2022-02-16] Accepted Paper /doc/2021.12.0664/v1

[2022-07-05] Printed Article /doc/2021.12.0664/v2