Retinal Image Segmentation for Diabetic Retinopathy Detection using U-Net Architecture

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Swapnil V. Deshmukh 1,* Apash Roy 2 Pratik Agrawal 3

1. Ram Meghe Institute of Technology & Research, Department of Computer Science and Engineering, Badnera, Amravati 444701, India

2. Lovely Professional University (LPU), Department of Computer Science and Application, Jalandhar 144001, Punjab, India

3. Department of Computer Science Engineering, Shri Ramdeobaba College of Engineering & Management Nagpur, 440013, India

* Corresponding author.


Received: 24 May 2022 / Revised: 15 Jun. 2022 / Accepted: 29 Jul. 2022 / Published: 8 Feb. 2023

Index Terms

Diabetic Retinopathy, Retinal Images, Blood Vessel, Region-based Segmentation, Deep Learning, DRIVE, STARE, and HRF


Diabetic retinopathy is one of the most serious eye diseases and can lead to permanent blindness if not diagnosed early. The main cause of this is diabetes. Not every diabetic will develop diabetic retinopathy, but the risk of developing diabetes is undeniable. This requires the early diagnosis of Diabetic retinopathy. Segmentation is one of the approaches which is useful for detecting the blood vessels in the retinal image. This paper proposed the three models based on a deep learning approach for recognizing blood vessels from retinal images using region-based segmentation techniques. The proposed model consists of four steps preprocessing, Augmentation, Model training, and Performance measure. The augmented retinal images are fed to the three models for training and finally, get the segmented image. The proposed three models are applied on publically available data set of DRIVE, STARE, and HRF. It is observed that more thin blood vessels are segmented on the retinal image in the HRF dataset using model-3. The performance of proposed three models is compare with other state-of-art-methods of blood vessels segmentation of DRIVE, STARE, and HRF datasets.

Cite This Paper

Swapnil V. Deshmukh, Apash Roy, Pratik Agrawal, "Retinal Image Segmentation for Diabetic Retinopathy Detection using U-Net Architecture", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.1, pp. 79-92, 2023. DOI:10.5815/ijigsp.2023.01.07


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