Glaucoma multi-classification using the novel syndrome mechanism-based dual-channel network

Abstract

Glaucoma is the first irreversible blinding eye disease in the world, so early detection and diagnosis are the key to the treatment of glaucoma. Glaucoma can be divided into normal, mild, moderate, and severe according to the severity of the disease. To diagnose the severity of glaucoma more efficiently and accurately, this paper proposes a multi-classification method using the novel Glaucoma Syndrome Mechanism-based Dual-Channel network (GSM-DCN). First, glaucoma anatomy, i.e., the optic disc-optic cup (OD-OC), tissue, and blood vessel features are extracted according to the clinical knowledge. Then, combined with the glaucoma anatomy features and the glaucoma syndrome mechanism, the GSM-DCN is constructed and trained. Finally, the GSM-DCN is linked to Softmax to realize a multi-classification model of glaucoma. The proposed method is verified on the clinical dataset of Dalian NO.3 People's Hospital and public Drishti-GS1 dataset and the corresponding accuracy can reach 99% and 98.9%, respectively. The proposed method can reduce the possibility of early misdiagnosis and missed diagnosis of glaucoma, help doctors make more accurate judgment, design personalized treatment plan, and prevent patients from further impaired visual function.