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An optimal way of subcutaneous vein detection using CNN: U-Net

Intravenous (IV) procedures are usually performed by nurses and medical practitioners through the patient’s veins. This is either to obtain blood samples or to administer drugs that cannot be taken orally. However, it has become an increasing concern since there are several factors affecting intrave...

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Main Author: Thong, Adrian Kheng Hoeng
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.utar.edu.my/4635/1/fyp_CS_2022_TAKH.pdf
http://eprints.utar.edu.my/4635/
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spelling my-utar-eprints.46352022-10-13T07:32:37Z An optimal way of subcutaneous vein detection using CNN: U-Net Thong, Adrian Kheng Hoeng Q Science (General) T Technology (General) Intravenous (IV) procedures are usually performed by nurses and medical practitioners through the patient’s veins. This is either to obtain blood samples or to administer drugs that cannot be taken orally. However, it has become an increasing concern since there are several factors affecting intravenous (IV) procedures to be done smoothly without hurting the patients repeatedly. This problem has been described as Peripheral Difficult Venous Access (PDVA) and performing such procedures repeatedly will heighten the patient’s anxiety level. Several state-of-art visualization and transformation techniques have been proposed for vein detection and finger vein detection for authentication purposes. However, deep learning has proved its ability and capability in performing automated tasks such as feature extraction, object recognition and detection. With considerations such as accuracy, automation, speed and being in real-time, we propose a deep learning solution (U-Net) to detect Near-Infrared (NIR) subcutaneous forearm veins. Results are then validated with groundtruths that are hand-plotted using Adobe Photoshop that has been verified by a certified radiologist. This report consists of an introduction part to the problem described, reviews of past related state-of-art works, proposing a deep learning framework and its variants as a feasible solution as well as performing performance analysis on the proposed framework. 2022-04-13 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4635/1/fyp_CS_2022_TAKH.pdf Thong, Adrian Kheng Hoeng (2022) An optimal way of subcutaneous vein detection using CNN: U-Net. Final Year Project, UTAR. http://eprints.utar.edu.my/4635/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Thong, Adrian Kheng Hoeng
An optimal way of subcutaneous vein detection using CNN: U-Net
description Intravenous (IV) procedures are usually performed by nurses and medical practitioners through the patient’s veins. This is either to obtain blood samples or to administer drugs that cannot be taken orally. However, it has become an increasing concern since there are several factors affecting intravenous (IV) procedures to be done smoothly without hurting the patients repeatedly. This problem has been described as Peripheral Difficult Venous Access (PDVA) and performing such procedures repeatedly will heighten the patient’s anxiety level. Several state-of-art visualization and transformation techniques have been proposed for vein detection and finger vein detection for authentication purposes. However, deep learning has proved its ability and capability in performing automated tasks such as feature extraction, object recognition and detection. With considerations such as accuracy, automation, speed and being in real-time, we propose a deep learning solution (U-Net) to detect Near-Infrared (NIR) subcutaneous forearm veins. Results are then validated with groundtruths that are hand-plotted using Adobe Photoshop that has been verified by a certified radiologist. This report consists of an introduction part to the problem described, reviews of past related state-of-art works, proposing a deep learning framework and its variants as a feasible solution as well as performing performance analysis on the proposed framework.
format Final Year Project / Dissertation / Thesis
author Thong, Adrian Kheng Hoeng
author_facet Thong, Adrian Kheng Hoeng
author_sort Thong, Adrian Kheng Hoeng
title An optimal way of subcutaneous vein detection using CNN: U-Net
title_short An optimal way of subcutaneous vein detection using CNN: U-Net
title_full An optimal way of subcutaneous vein detection using CNN: U-Net
title_fullStr An optimal way of subcutaneous vein detection using CNN: U-Net
title_full_unstemmed An optimal way of subcutaneous vein detection using CNN: U-Net
title_sort optimal way of subcutaneous vein detection using cnn: u-net
publishDate 2022
url http://eprints.utar.edu.my/4635/1/fyp_CS_2022_TAKH.pdf
http://eprints.utar.edu.my/4635/
_version_ 1748184792786010112
score 13.211869

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