LDMRes-Net: Enabling Real-Time Disease Monitoring through Efficient Image Segmentation
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Retinal eye diseases can lead to irreversible vision loss in both eyes if not diagnosed and treated earlier. Owing to the complexities of retinal diseases, the likelihood that retinal images would contain two or more abnormalities is very high. The current deep learning algorithms used for segmenting retinal images with multiple labels and features suffer from inadequate detection accuracy and a lack of generalizability. In this paper, we propose a lightweight and efficient network, featuring dual multi-residual connections to enhance segmentation performance while minimizing computational cost. The proposed network is evaluated on eight publicly available retinal image datasets and achieved promising segmentation results, which demonstrate the effectiveness of the proposed network for retinal image analysis tasks. The proposed network's lightweight and efficient design makes it a promising candidate for real-time retinal image analysis applications.
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