Apash Roy

Work place: Lovely Professional University (LPU), Department of Computer Science and Application, Jalandhar 144001, Punjab, India

E-mail: apash.23550@lpu.co.in

Website: https://orcid.org/0000-0001-6340-6604

Research Interests: Data Structures and Algorithms, Pattern Recognition, Computational Learning Theory, Computational Science and Engineering


Dr. Apash Roy currently working as Associate Professor, Lovely Professional University, Jalandhar, Punjab, India. He is also working as Research Advisory committee and Panel Member of research evaluation. Supervising several research projects of different research scholars, also supervised numerous academic projects for UG and PG students. Earlier, He worked in ICFAI University Tripura, Agartala, India, and University of North Bengal, Siliguri, Darjeeling, India as Assistant Professor. Have a total Teaching and Research Experience of 12+years. He authored or co-authored several research articles in reputed national and international Journal and Conferences. He filed several patents in Indian Patent Office for granting. Worked as resource person for several Conference and Workshops in the subject area of open-source software, machine learning, Data Science, etc. He worked as reviewer and session chair for several renowned Journals. Some of his research interests are - Pattern Recognition, Machine learning, Data science.

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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