Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach

The mammography image eccentric area is the breast density percentage measurement. The technical challenge of quantification in radiology leads to misinterpretation in screening. Data feedback from society, institutional, and industry shows that quantification and segmentation frameworks have rap...

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Main Author:	Othman, Khairulnizam
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/8430/1/24p%20KHAIRULNIZAM%20OTHMAN.pdf http://eprints.uthm.edu.my/8430/2/KHAIRULNIZAM%20OTHMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8430/3/KHAIRULNIZAM%20OTHMAN%20WATERMARK.pdf
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spelling	my-uthm-ep.84302023-02-26T07:26:10Z Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach 2022-08 Othman, Khairulnizam T Technology (General) The mammography image eccentric area is the breast density percentage measurement. The technical challenge of quantification in radiology leads to misinterpretation in screening. Data feedback from society, institutional, and industry shows that quantification and segmentation frameworks have rapidly become the primary methodologies for structuring and interpreting mammogram digital images. Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. In combination, mammogram quantification creates a long-standing focus area. The algorithm proposed must reduce complexity and target data points distributed in iterative, and boost cluster centroid merged into a single updating process to evade the large storage requirement. The mammogram database's initial test segment is critical for evaluating performance and determining the Area Under the Curve (AUC) to alias with medical policy. In addition, a new image clustering algorithm anticipates the need for largescale serial and parallel processing. There is no solution on the market, and it is necessary to implement communication protocols between devices. Exploiting and targeting utilization hardware tasks will further extend the prospect of improvement in the cluster. Benchmarking their resources and performance is required. Finally, the medical imperatives cluster was objectively validated using qualitative and quantitative inspection. The proposed method should overcome the technical challenges that radiologists face. 2022-08 Thesis http://eprints.uthm.edu.my/8430/ http://eprints.uthm.edu.my/8430/1/24p%20KHAIRULNIZAM%20OTHMAN.pdf text en public http://eprints.uthm.edu.my/8430/2/KHAIRULNIZAM%20OTHMAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8430/3/KHAIRULNIZAM%20OTHMAN%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Othman, Khairulnizam Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
description	The mammography image eccentric area is the breast density percentage measurement. The technical challenge of quantification in radiology leads to misinterpretation in screening. Data feedback from society, institutional, and industry shows that quantification and segmentation frameworks have rapidly become the primary methodologies for structuring and interpreting mammogram digital images. Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. In combination, mammogram quantification creates a long-standing focus area. The algorithm proposed must reduce complexity and target data points distributed in iterative, and boost cluster centroid merged into a single updating process to evade the large storage requirement. The mammogram database's initial test segment is critical for evaluating performance and determining the Area Under the Curve (AUC) to alias with medical policy. In addition, a new image clustering algorithm anticipates the need for largescale serial and parallel processing. There is no solution on the market, and it is necessary to implement communication protocols between devices. Exploiting and targeting utilization hardware tasks will further extend the prospect of improvement in the cluster. Benchmarking their resources and performance is required. Finally, the medical imperatives cluster was objectively validated using qualitative and quantitative inspection. The proposed method should overcome the technical challenges that radiologists face.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Othman, Khairulnizam
author_facet	Othman, Khairulnizam
author_sort	Othman, Khairulnizam
title	Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
title_short	Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
title_full	Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
title_fullStr	Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
title_full_unstemmed	Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
title_sort	quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/8430/1/24p%20KHAIRULNIZAM%20OTHMAN.pdf http://eprints.uthm.edu.my/8430/2/KHAIRULNIZAM%20OTHMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8430/3/KHAIRULNIZAM%20OTHMAN%20WATERMARK.pdf
_version_	1776103343913959424

Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach

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