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RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening

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Abstract

Diabetic Retinopathy (DR) is a primitive cause of blindness in diabetic patients. There are chances for DR in certain cases, due to the damage in the retinal vascular area. It may lead to blindness or loss of vision if DR is not detected. At the final stage, there is a threat of loss of vision. During the initial stage of this chronic disease, it has been observed that no symptoms are required to determine the loss of vision. Hence, there is a need for better and early diagnosis of the blood vessels to prevent vision loss in diabetic patients. The primary task of early diagnosis is to carry out a proper segmentation process. But, segmenting vasculture from retinal photographs is a vital laborious effort. Therefore, blood vessel segmentation of fundus images has grown in favor among researchers. There are many existing methods to process bio-medical retinal images, and most of them are computationally intensive and difficult to deploy in real-time. The objective of this work is to develop a novel and efficient deep learning framework to perform the segmentation with the U-Net as a primary module. The U-Net architecture has been modified so that the model would be lightweight and the model is cost efficient in terms of time and space complexity. The proposed method, DRLeU- Net, is evaluated using a publicly available color retinal fundus images dataset. The model was trained using the k-fold cross-validation technique. The proposed method shows a promising results of 0.988 AUC (Area Under the Curve) and 0.94 IOU (Intersection over Union when compared with the existing methods.The robust and efficient deep learning framework has been deployed to do the segmentation process efficiently and it has evauated with the expremental results by comparing with the existing methodologies.

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  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Sathyavani Addanki & D. Sumathi

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  1. Sathyavani Addanki
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  2. D. Sumathi
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Correspondence to D. Sumathi.

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Addanki, S., Sumathi, D. RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening. Multimed Tools Appl 84, 6113–6134 (2025). https://doi.org/10.1007/s11042-024-19159-y

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