Assisting Glaucoma Screening Process Using Feature Excitation and Information Aggregation Techniques in Retinal Fundus Images

The rapidly increasing trend of retinal diseases needs serious attention, worldwide. Glaucoma is a critical ophthalmic disease that can cause permanent vision impairment. Typically, ophthalmologists diagnose glaucoma using manual assessments which is an error-prone, subjective, and time-consuming approach. Therefore, the development of automated methods is crucial to strengthen and assist the existing diagnostic methods. In fundus imaging, optic cup (OC) and optic disc (OD) segmentation are widely accepted by researchers for glaucoma screening assistance. Many research studies proposed artificial intelligence (AI) based decision support systems for glaucoma diagnosis. However, existing AI-based methods show serious limitations in terms of accuracy and efficiency. Variations in backgrounds, pixel intensity values, and object size make the segmentation challenging. Particularly, OC size is usually very small with unclear boundaries which makes its segmentation even more difficult. To effectively address these problems, a novel feature excitation-based dense segmentation network (FEDS-Net) is developed to provide accurate OD and OC segmentation. FEDS-Net employs feature excitation and information aggregation (IA) mechanisms for enhancing the OC and OD segmentation performance. FEDS-Net also uses rapid feature downsampling and efficient convolutional depth for diverse and efficient learning of the network, respectively. The proposed framework is comprehensively evaluated on three open databases: REFUGE, Drishti-GS, and Rim-One-r3. FEDS-Net achieved outperforming segmentation performance compared with state-of-the-art methods. A small number of required trainable parameters (2.73 million) also confirms the superior computational efficiency of our proposed method