BranchFusionNet: An energy-efficient lightweight framework for superior retinal vessel segmentation

In the rapidly advancing field of medical image analysis, accurate and efficient segmentation of retinal vessels is paramount for diagnosing ocular diseases, especially diabetic retinopathy. With the increasing emphasis on environmental sustainability, this paper presents BranchFusionNet, a novel lightweight neural network architecture tailored for retinal vessel segmentation. Embodying the principles of energy conservation, BranchFusionNet integrates multi-branch and lightweight dual-branch modules to optimize computational demands without sacrificing segmentation precision. This study not only contributes to the domain of retinal vessel segmentation but also showcases the potential of crafting energy-conscious deep learning methodologies in medical imaging applications.