A novel lightweight deep learning approach for simultaneousoptic cup and optic disc segmentation in glaucoma detection

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages.Thecup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it isdetermined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation oftheOC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted ingreat success on this task. However, most existing methods either have unsatisfactory segmentation accuracy orhigh time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation oftheOC and OD, where we haveadoptedfuzzy learning andamulti-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared tomost state-of-the-art approaches in terms of both training time and segmentation accuracy. © 2024the Author(s).