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Jurnal SINTA 2

Breast Cancer Diagnosis in Women Using Neural Networks and Deep Learning

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 2 (2022) · pp. 152–166

DOI
10.5614/itbj.ict.res.appl.2022.16.2.4
ISSN
2338-5499, 2337-5787
Published
2022-09-09
Publisher
DRI - ITB
Language
eng

Abstract

Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98.25% and sensitivity of 99.5% after 80 iterations.

Keywords

Collaboration network of the authors

7 authors · 15 co-authorship links · 13 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Federal University Oye Ekiti
  • North-West University
  • The Federal Polytechnic, Ado-Ekiti
  • Federal University of Technology and Environmental Sciences
  • University of KwaZulu-Natal
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ojo Femi AjewoleFederal University of Technology and Environmental Sciences2
Stephen Alaba MogajiFederal University Oye Ekiti2
Samson Adebisi AkinpeluUniversity of KwaZulu-Natal2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Breast Cancer Diagnosis in Women Using Neural Networks and Deep Learning</dc:title>
	<dc:creator>Fagbuagun, Ojo</dc:creator>
	<dc:creator>Folorunsho, Olaiya</dc:creator>
	<dc:creator>Adewole, Lawrence</dc:creator>
	<dc:creator>Akin-Olayemi, Titilayo</dc:creator>
	<dc:subject xml:lang="en-US">breast-cancer</dc:subject>
	<dc:subject xml:lang="en-US">diagnosis</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">mammography</dc:subject>
	<dc:subject xml:lang="en-US">neural network</dc:subject>
	<dc:description xml:lang="en-US">Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98.25% and sensitivity of 99.5% after 80 iterations. </dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-09-09</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/17454</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.2.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 2 (2022); 152-166</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2022.16.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/17454/5951</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of ICT Research and Applications</dc:rights>
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