Exploring deep feature-blending capabilities to assist glaucoma screening

Abstract

Over the last three decades, computer vision has had a vital role in the healthcare sector by providing soft computing-based robust and intelligent diagnostic solutions. Glaucoma is a critical ophthalmic disease that can trigger irreversible loss of vision. The number of patients with glaucoma is increasing dramatically worldwide. Manual ophthalmic assessment of glaucoma detection is a tedious, error-prone, time-consuming, and subjective task. Therefore, computer-assisted automatic glaucoma diagnosis methods are required to strengthen existing diagnostic methods with their robust performance. Optic disc (OD) and optic cup (OC) segmentation have a key role in glaucoma detection. Accurate segmentation of the OD and OC provides valuable computational and clinical details that can substantially assist in the glaucoma screening process. Retinal fundus images have extensive variations in terms of size, shape, pixel intensity values, and background effects that make segmentation challenging. To mitigate these challenges, we developed two novel networks for accurate OD and OC segmentation. An efficient shallow segmentation network (ESS-Net) is the base network whereas a feature-blending-based shallow segmentation network (FBSS-Net) is the final network of this work. ESS-Net is a shallow architecture with a maximum-depth semantic preservation block for accurate segmentation, while FBSS-Net uses internal and external feature blending to improve overall segmentation performance.To confirm their effectiveness, we evaluated both networks using four publicly available datasets; REFUGE, Drions-DB, Drishti-GS, and Rim-One-r3. The proposed methods exhibited excellent segmentation performance, requiring a small number of trainable parameters (3.02 million parameters).