Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. (30th November 2022)

• Record Type: Journal Article
• Authors: Haider, Adnan; Arsalan, Muhammad; Lee, Min Beom; Owais, Muhammad; Mahmood, Tahir; Sultan, Haseeb; Park, Kang Ryoung
• Is Part Of: Expert systems with applications. Volume 207(2022)
• Publication Date: 2022-11-30
• Subjects: Artificial intelligence -- Optic cup and optic disc segmentation -- Glaucoma screening -- Computer-aided diagnosis -- SLS-Net and SLSR-Net; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117968
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Glaucoma is one of the most common chronic diseases that may lead to irreversible vision loss. The number of patients with permanent vision loss due to glaucoma is expected to increase at an alarming rate in the near future. A considerable amount of research is being conducted on computer-aided diagnosis for glaucoma. Segmentation of the optic cup (OC) and optic disc (OD) is usually performed to distinguish glaucomatous and non-glaucomatous cases in retinal fundus images. However, the OC boundaries are quite non-distinctive; consequently, the accurate segmentation of the OC is substantially challenging, and the OD segmentation performance also needs to be improved. To overcome this problem, we propose two networks, separable linked segmentation network (SLS-Net) and separable linked segmentation residual network (SLSR-Net), for accurate pixel-wise segmentation of the OC and OD. In SLS-Net and SLSR-Net, a large final feature map can be maintained in our networks, which enhances the OC and OD segmentation performance by minimizing the spatial information loss. SLSR-Net employs external residual connections for feature empowerment. Both proposed networks comprise a separable convolutional link to enhance computational efficiency and reduce the cost of network. Even with a few trainable parameters, the proposed architecture is capable of providing high segmentation accuracy. The segmentation performances of the proposed networks were evaluated on four publicly available retinal fundus image datasets: Drishti-GS, REFUGE, Rim-One-r3, and Drions-DB which confirmed that our networks outperformed the state-of-the-art segmentation architectures.