Small sample color fundus image quality assessment based on gcforest

Color fundus image quality greatly influence the doctors’ diagnostic accuracy. However, the problems of imbalance data and small sample are the key issues of the color fundus images quality assessment. Hence, this paper purposes a small sample color fundus image quality assessment based on gcforest to solve these problems. Firstly, this paper extracts color and texture features to represent the quality of color fundus image. Next, re-sampling process is used to re-balance training data. Thirdly, the training data after re-balanced is sent to train gcforest which is a forest integration model. Finally, the trained gcforest which is good for small sample problem is used to evaluate color fundus images quality. Experiments demonstrate that the proposed method not only in color fundus image quality assessment but also in glaucoma classification task get good results.