Automatic Cataract Classification Based on Multi-feature Fusion and SVM

According to the World Health Organization (WHO), cataract is the most common cause of vision loss and blindness. In this paper, we propose a scheme to grade cataract into six classifications for precise automatic cataract diagnosis. We extract two kinds of features: texture features by gray-level co-occurrence matrix (GLCM) and high-level features via the pre-trained residual network (ResNet). Then, the two kinds of features are fused in a way of dimension expansion, which texture feature vectors are added to the tail of high-level feature vectors. Next, the fused feature vectors are put into support vector machine (SVM) to train and verify. Our scheme can achieve 91.5% average accuracy on cataract for six classifications. In addition, our proposed scheme outperforms the existing methods for four classifications significantly.