UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 10
October-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2210360


Registration ID:
503743

Page Number

d385-d390

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Title

Performance Analysis of Retinal Blood Vessel Image Segmentation using Deep Learning techniques

Abstract

The state of the human eye's vascular network is an essential diagnostic element in ophthalmology. The properties of retinal vessels reflect a patient's health condition and aid in the diagnosis of certain disorders such as diabetic maculopathy, diabetic retinopathy, and hypertension. In practice, retinal image data has a high dimensionality, resulting in data of tremendous magnitude. Deep Learning (DL) approaches are assisting in the development of intelligent retinal image analysis tools because morphological retinal image datasets may be evaluated in a broad and non-invasive manner. Accurate identification and treatment of these disorders can help people avoid total blindness. Because of their improved efficiency and accuracy, deep learning techniques have recently been quickly used to retinal vascular segmentation. By measuring the performance of various techniques, the better deep learning technique will be decided by doing the analysis of various parameters like accuracy, F1-score, sensitivity, specificity.

Key Words

Fundus image, Dataset, Deep learning , CNN , U-net.

Cite This Article

"Performance Analysis of Retinal Blood Vessel Image Segmentation using Deep Learning techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.d385-d390, October-2022, Available :http://www.jetir.org/papers/JETIR2210360.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Performance Analysis of Retinal Blood Vessel Image Segmentation using Deep Learning techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppd385-d390, October-2022, Available at : http://www.jetir.org/papers/JETIR2210360.pdf

Publication Details

Published Paper ID: JETIR2210360
Registration ID: 503743
Published In: Volume 9 | Issue 10 | Year October-2022
DOI (Digital Object Identifier):
Page No: d385-d390
Country: Vizianagaram, Andhra Pradesh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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