Detecting glaucoma from fundus images using ensemble learning

Glaucomatous changes of the optic nerve head could be detected from fundus images. Focusing on optic nerve head appearance, and its difference from healthy images, altogether with the availability of plenty of such images in public fundus image databases, these images are ideal sources for artificial intelligence methods applications. In this work, we used ensemble learning methods and compared them with various single CNN models (VGG-16, ResNet-50, and MobileNet). The models were trained on images from REFUGE public dataset. The average voting ensemble method outperformed all mentioned models with 0.98 accuracy. In the AUC metric, the average voting ensemble method outperformed VGG-16 and MobileNet models, which had significantly weaker performance when used alone. The best results were observed using the ResNet-50 model. These results confirmed the significant potential of ensemble learning in enhancing the overall predictive performance in glaucomatous changes detection, but the overall performance could be negatively affected when models with weaker prediction performance are included.