Abstract
Glaucoma has been credited to be the foremost cause of preventable loss of sight in the world second only to cataract. Its effect on the eye is usually irreversible and can only be prevented by early detection. In this paper, we developed a glaucoma detection technique. This technique includes a modified U-net model and an extreme gradient boost (XGB) algorithm. The modified U-net model was utilized to segment both the optic cup and the optic disc from the fundus images. The extreme gradient boost algorithm was utilized to analyze extracted features from segmented optic cups and discs and hence detect glaucoma. The modified U-net model was both trained and tested on the DRIONS, DRISHTI-GS, RIM-ONE-V2 and the RIM-ONE-V3 databases. When tested for optic disc segmentation on the four databases, the model achieved the following average dice-scores: 0.96 on RIM-ONE-V3, 0.97 on RIM-ONE-V2, 0.96 on DRIONS, and 0.97 on DRISHTI-GS. The XGB algorithm achieved an accuracy of 88 % and an AUC-ROC of 92.9 % in detecting glaucoma from the segmented optic disc and optic cup. The proposed glaucoma detection technique achieves a state-of-the-art accuracy and useful for observing structural changes in optic cup and optic disc. The advantage of the proposed modified U-net model is that it has much fewer parameters to be trained when compared to the original U-net model and hence faster training time and cheaper training cost.