A deep learning framework for glaucoma detection based on robust optic disc segmentation and transfer learning

Abstract

AbstractGlaucoma is a common ocular disorder, inflicting blindness in millions of people, early detection of which can reduce adverse outcomes. This paper presents a two‐stage deep learning framework UNet‐SNet, for glaucoma detection. Initially, each fundus image is segmented into GMM super pixels and the Region of Interest (RoI) is separated by Cuckoo Search Optimization (CSO). In the first stage, a regularized UNet is trained with RoIs for OD segmentation. In the second stage, a SqueezeNet is fine‐tuned with deep features of the ODs to discriminate fundus images into glaucomotousor Normal. The UNet is trained and tested with the RIGA and RIM‐ONEv2 datasets, achieving 97.84% and 99.85% accuracies respectively. The classifier is trained with the ODs segmented from the RIM‐ONEv2 dataset and tested with ACRIMA, Drishti‐GS1 and RIM‐ONEv1 datasets accomplishing 99.86%, 97.05% and 100% accuracies respectively. Performance evaluations and complexity analyses with state‐of‐the‐art systems demonstrate the superiority of the proposed model.