Retinal vessel segmentation based on neural network application

Automated segmentation of retinal vessels plays an important role in diagnosing diabetic diseases. In this paper, I propose the convolutional neural network (U-Net) to yield more precise segmentations of retinal vessels from various fundus images. The performance of this neural network was first tes...

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Main Author: Tan, Chin Guan
Other Authors: Jiang Xudong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/138852
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1388522023-07-07T18:36:01Z Retinal vessel segmentation based on neural network application Tan, Chin Guan Jiang Xudong School of Electrical and Electronic Engineering exdjiang@ntu.edu.sg Engineering Automated segmentation of retinal vessels plays an important role in diagnosing diabetic diseases. In this paper, I propose the convolutional neural network (U-Net) to yield more precise segmentations of retinal vessels from various fundus images. The performance of this neural network was first tested on the DRIVE database, and it achieved a relatively high score in terms of area under the Receiver Operating Characteristic (ROC) curve, with an astounding result of 0.9790, in comparison to the other existing methods published. On the STARE database, this method also yield satisfying results. This shows that the U-net architecture is a very effective and efficient model to aid in the early diagnosis of diseases. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-13T06:20:53Z 2020-05-13T06:20:53Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138852 en A3102-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
spellingShingle Engineering
Tan, Chin Guan
Retinal vessel segmentation based on neural network application
description Automated segmentation of retinal vessels plays an important role in diagnosing diabetic diseases. In this paper, I propose the convolutional neural network (U-Net) to yield more precise segmentations of retinal vessels from various fundus images. The performance of this neural network was first tested on the DRIVE database, and it achieved a relatively high score in terms of area under the Receiver Operating Characteristic (ROC) curve, with an astounding result of 0.9790, in comparison to the other existing methods published. On the STARE database, this method also yield satisfying results. This shows that the U-net architecture is a very effective and efficient model to aid in the early diagnosis of diseases.
author2 Jiang Xudong
author_facet Jiang Xudong
Tan, Chin Guan
format Final Year Project
author Tan, Chin Guan
author_sort Tan, Chin Guan
title Retinal vessel segmentation based on neural network application
title_short Retinal vessel segmentation based on neural network application
title_full Retinal vessel segmentation based on neural network application
title_fullStr Retinal vessel segmentation based on neural network application
title_full_unstemmed Retinal vessel segmentation based on neural network application
title_sort retinal vessel segmentation based on neural network application
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/138852
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