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

Author:

Raza Ali,Adnan Sharjeel,Ishaq Muhammad,Kim Hyung Seok,Naqvi Rizwan AliORCID,Lee Seung-WonORCID

Abstract

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.

Funder

Ministry of Science and ICT (MSIT), South Korea

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A multi-scale convolutional neural network with adaptive weight fusion strategy for assisting glaucoma screening;Biomedical Signal Processing and Control;2024-12

2. Glaucoma Detection From Retinal Fundus Images;2024 International Conference on Communication, Computing and Internet of Things (IC3IoT);2024-04-17

3. Automated Tool Support for Glaucoma Identification With Explainability Using Fundus Images;IEEE Access;2024

4. Contextual Augmentation Based on Metric-Guided Features for Ocular Axial Length Prediction;Mathematics;2023-07-07

5. Performance Analysis of Deep Learning based Segmentation of Retinal Lesions in Fundus Images;2023 Second International Conference on Electronics and Renewable Systems (ICEARS);2023-03-02

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