Swapnil V. Deshmukh

Work place: Ram Meghe Institute of Technology & Research, Department of Computer Science and Engineering, Badnera, Amravati 444701, India

E-mail: jaykantdeshmukh@gmail.com

Website: https://orcid.org/0000-0002-3246-0998

Research Interests: Data Structures and Algorithms, Computational Learning Theory


Swapnil Deshmukh is from Maharashtra India and pursuing Ph. D. in Lovely Professional University on the topic ‘Early Detection of Diabetic Retinopathy’, Currently he is working as an assistant Professor in Department of Computer Science and Engineering, Prof. Ram Meghe Institute of Technology & Research Badnera Amravati, 444701 India He completed his bachelor's degree in computer science and engineering and master degree in Embedded System and Computing. Currently working on Data Science, Machine learning and deep Learning.

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

By Swapnil V. Deshmukh Apash Roy Pratik Agrawal

DOI: https://doi.org/10.5815/ijigsp.2023.01.07, Pub. Date: 8 Feb. 2023

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.

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