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Leveraging u-net architecture for accurate localization in brain tumor segmentation

This study presents an approach based on deep learning to segment brain tumors in medical imaging accurately. The segmentation of brain tumors plays a crucial role in diagnosing, planning treatments, and monitoring disease progression. However, existing methods have limitations such as time-consumin...

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Main Authors:	Poo, Jeckey Ng Kah, Saealal, Muhammad Salihin, Mohd Zamri, Ibrahim, Marlina, Yakno
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/41910/1/Leveraging%20u-net%20architecture%20for%20accurate%20localization.pdf http://umpir.ump.edu.my/id/eprint/41910/2/Leveraging%20u-net%20architecture%20for%20accurate%20localization%20in%20brain%20tumor%20segmentation_ABS.pdf http://umpir.ump.edu.my/id/eprint/41910/ https://doi.org/10.1109/ITIS59651.2023.10419915
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spelling	my.ump.umpir.419102024-08-30T00:15:31Z http://umpir.ump.edu.my/id/eprint/41910/ Leveraging u-net architecture for accurate localization in brain tumor segmentation Poo, Jeckey Ng Kah Saealal, Muhammad Salihin Mohd Zamri, Ibrahim Marlina, Yakno T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This study presents an approach based on deep learning to segment brain tumors in medical imaging accurately. The segmentation of brain tumors plays a crucial role in diagnosing, planning treatments, and monitoring disease progression. However, existing methods have limitations such as time-consuming procedures, inadequate accuracy, and delayed detection. The U-Net model architecture, a widely used convolutional neural network (CNN) for medical image segmentation tasks, was employed to segment brain tumors in CT and MRI scans to overcome these challenges. The performance of the U-Net model was evaluated on datasets consisting of 32, 64, and 128 slices, respectively. The results demonstrated the achievement of the highest percentage of mean Intersection Over Union (IOU), with an impressive 80.89% for brain tumor segmentation. These results outperformed other existing methods. The proposed model exhibits the potential to reduce manual segmentation time and subjectivity while enhancing the accuracy of brain tumor diagnosis, treatment planning, and disease monitoring. This research contributes to the field by addressing the problem of brain tumor detection and showcasing the promising results attained using deep learning techniques. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41910/1/Leveraging%20u-net%20architecture%20for%20accurate%20localization.pdf pdf en http://umpir.ump.edu.my/id/eprint/41910/2/Leveraging%20u-net%20architecture%20for%20accurate%20localization%20in%20brain%20tumor%20segmentation_ABS.pdf Poo, Jeckey Ng Kah and Saealal, Muhammad Salihin and Mohd Zamri, Ibrahim and Marlina, Yakno (2023) Leveraging u-net architecture for accurate localization in brain tumor segmentation. In: Proceeding - IEEE 9th Information Technology International Seminar, ITIS 2023. 9th IEEE Information Technology International Seminar, ITIS 2023 , 18 - 20 October 2023 , Batu Malang. pp. 1-6. (197293). ISBN 979-835030683-5 (Published) https://doi.org/10.1109/ITIS59651.2023.10419915
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Poo, Jeckey Ng Kah Saealal, Muhammad Salihin Mohd Zamri, Ibrahim Marlina, Yakno Leveraging u-net architecture for accurate localization in brain tumor segmentation
description	This study presents an approach based on deep learning to segment brain tumors in medical imaging accurately. The segmentation of brain tumors plays a crucial role in diagnosing, planning treatments, and monitoring disease progression. However, existing methods have limitations such as time-consuming procedures, inadequate accuracy, and delayed detection. The U-Net model architecture, a widely used convolutional neural network (CNN) for medical image segmentation tasks, was employed to segment brain tumors in CT and MRI scans to overcome these challenges. The performance of the U-Net model was evaluated on datasets consisting of 32, 64, and 128 slices, respectively. The results demonstrated the achievement of the highest percentage of mean Intersection Over Union (IOU), with an impressive 80.89% for brain tumor segmentation. These results outperformed other existing methods. The proposed model exhibits the potential to reduce manual segmentation time and subjectivity while enhancing the accuracy of brain tumor diagnosis, treatment planning, and disease monitoring. This research contributes to the field by addressing the problem of brain tumor detection and showcasing the promising results attained using deep learning techniques.
format	Conference or Workshop Item
author	Poo, Jeckey Ng Kah Saealal, Muhammad Salihin Mohd Zamri, Ibrahim Marlina, Yakno
author_facet	Poo, Jeckey Ng Kah Saealal, Muhammad Salihin Mohd Zamri, Ibrahim Marlina, Yakno
author_sort	Poo, Jeckey Ng Kah
title	Leveraging u-net architecture for accurate localization in brain tumor segmentation
title_short	Leveraging u-net architecture for accurate localization in brain tumor segmentation
title_full	Leveraging u-net architecture for accurate localization in brain tumor segmentation
title_fullStr	Leveraging u-net architecture for accurate localization in brain tumor segmentation
title_full_unstemmed	Leveraging u-net architecture for accurate localization in brain tumor segmentation
title_sort	leveraging u-net architecture for accurate localization in brain tumor segmentation
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/41910/1/Leveraging%20u-net%20architecture%20for%20accurate%20localization.pdf http://umpir.ump.edu.my/id/eprint/41910/2/Leveraging%20u-net%20architecture%20for%20accurate%20localization%20in%20brain%20tumor%20segmentation_ABS.pdf http://umpir.ump.edu.my/id/eprint/41910/ https://doi.org/10.1109/ITIS59651.2023.10419915
_version_	1822924594826182656
score	13.232414

Leveraging u-net architecture for accurate localization in brain tumor segmentation

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