Optic disc and cup segmentation through fuzzy broad learning system for glaucoma screening

Glaucoma is an ocular disease that causes permanent blindness if not cured at an early stage. Cup-to-disc ratio (CDR), obtained by dividing the height of optic cup (OC) with the height of optic disc (OD), is a widely adopted metric used for glaucoma screening. Therefore, accurately segmenting OD and OC is crucial for calculating a CDR. Most methods have employed deep learning methods for the segmentation of OD and OC. However, these methods are very time-consuming. We present a new fuzzy broad learning system-based technique for OD and OC segmentation with glaucoma screening. We comprehensively integrated extracting a region of interest (ROI) from RGB images, data augmentation, extracting red and green channel images, and inputting them to the two separate fuzzy broad learning system-based neural networks for segmenting the OD and OC, respectively, and then calculated CDR. Experiments show that our fuzzy broad learning system-based technique outperforms many state-of-the-art methods.