Abstract: Automatic analysis of fundus images is the foundation of computer-aided glaucoma screening and diagnosis. To improve the accuracy of glaucoma screening, a novel glaucoma detection method is proposed based on the multi-channel feature aggregation in fundus images. Firstly, multi-channel features are computed based on color distribution, multi-scale Gabor filters and oriented gradient histogram, and they describe the tiny changes in the morphology and structure of the optic disc. Secondly, a random forest classifier is developed to detect glaucoma based on the multi-channel features and ensemble learning technology. Finally, the glaucoma detection algorithm is tested on two challenging glaucoma datasetss and obtains values of the area under the curve with 0.869 0 and 0.820 4, respectively. The experimental results show that the proposed method can improve sensitivity and specificity simultaneously.