Breast Cancer Detection via Global and Local Features using Digital Histology Images

Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT) is a primary symptom and may lead to BC. Digital histology (DH) image modality is a gold standard medical test for a definite diagnosis of BC. Traditionally, DH images are visually examined by two or...

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Autores principales:	Ghulam Murtaza, Ainuddin Wahid Abdul Wahab, Ghulam Raza, Liyana Shuib
Formato:	article
Lenguaje:	EN
Publicado:	Sukkur IBA University 2021
Materias:	Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/addf8ed2e5624606b266b04e0cfeed85
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spelling	oai:doaj.org-article:addf8ed2e5624606b266b04e0cfeed852021-11-11T10:06:48ZBreast Cancer Detection via Global and Local Features using Digital Histology Images10.30537/sjcms.v5i1.7692520-07552522-3003https://doaj.org/article/addf8ed2e5624606b266b04e0cfeed852021-03-01T00:00:00Zhttp://localhost:8089/sibajournals/index.php/sjcms/article/view/769https://doaj.org/toc/2520-0755https://doaj.org/toc/2522-3003 Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT) is a primary symptom and may lead to BC. Digital histology (DH) image modality is a gold standard medical test for a definite diagnosis of BC. Traditionally, DH images are visually examined by two or more pathologists to come up with a consensus for authentic BC detection which may cause a high error rate. Therefore, researchers had developed automated BC detection models using a machine learning (ML) based approach. Thus, this study aims to develop a BC detection model through ten feature extraction methods which extract both local and global type features from publicly available breast histology dataset. The extracted features are sorted by their weights, which are computed by the neighborhood component analysis method. A feature selection algorithm is developed to find the minimum number of discriminating features, evaluated through seven heterogeneous traditional ML classifiers. The proposed ML-based BC detection model acquired 90% accuracy for the initial testing set using 51 Harris features. Whereas, for the extended testing set, only three Harris features is shown 93% accuracy. The proposed BC detection model can assist the doctor in giving a second opinion. Ghulam MurtazaAinuddin Wahid Abdul WahabGhulam RazaLiyana ShuibSukkur IBA UniversityarticleComputer engineering. Computer hardwareTK7885-7895MathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENSukkur IBA Journal of Computing and Mathematical Sciences, Vol 5, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 Ghulam Murtaza Ainuddin Wahid Abdul Wahab Ghulam Raza Liyana Shuib Breast Cancer Detection via Global and Local Features using Digital Histology Images
description	Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT) is a primary symptom and may lead to BC. Digital histology (DH) image modality is a gold standard medical test for a definite diagnosis of BC. Traditionally, DH images are visually examined by two or more pathologists to come up with a consensus for authentic BC detection which may cause a high error rate. Therefore, researchers had developed automated BC detection models using a machine learning (ML) based approach. Thus, this study aims to develop a BC detection model through ten feature extraction methods which extract both local and global type features from publicly available breast histology dataset. The extracted features are sorted by their weights, which are computed by the neighborhood component analysis method. A feature selection algorithm is developed to find the minimum number of discriminating features, evaluated through seven heterogeneous traditional ML classifiers. The proposed ML-based BC detection model acquired 90% accuracy for the initial testing set using 51 Harris features. Whereas, for the extended testing set, only three Harris features is shown 93% accuracy. The proposed BC detection model can assist the doctor in giving a second opinion.
format	article
author	Ghulam Murtaza Ainuddin Wahid Abdul Wahab Ghulam Raza Liyana Shuib
author_facet	Ghulam Murtaza Ainuddin Wahid Abdul Wahab Ghulam Raza Liyana Shuib
author_sort	Ghulam Murtaza
title	Breast Cancer Detection via Global and Local Features using Digital Histology Images
title_short	Breast Cancer Detection via Global and Local Features using Digital Histology Images
title_full	Breast Cancer Detection via Global and Local Features using Digital Histology Images
title_fullStr	Breast Cancer Detection via Global and Local Features using Digital Histology Images
title_full_unstemmed	Breast Cancer Detection via Global and Local Features using Digital Histology Images
title_sort	breast cancer detection via global and local features using digital histology images
publisher	Sukkur IBA University
publishDate	2021
url	https://doaj.org/article/addf8ed2e5624606b266b04e0cfeed85
work_keys_str_mv	AT ghulammurtaza breastcancerdetectionviaglobalandlocalfeaturesusingdigitalhistologyimages AT ainuddinwahidabdulwahab breastcancerdetectionviaglobalandlocalfeaturesusingdigitalhistologyimages AT ghulamraza breastcancerdetectionviaglobalandlocalfeaturesusingdigitalhistologyimages AT liyanashuib breastcancerdetectionviaglobalandlocalfeaturesusingdigitalhistologyimages
_version_	1718439247738830848

Breast Cancer Detection via Global and Local Features using Digital Histology Images

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