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Breast Cancer Classification Based on Mammogram Images Using CNN Method with NASNet Mobile Model

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 3 (2025) · pp. 293–304

DOI
10.22146/ijccs.98187
ISSN
2460-7258, 1978-1520
Published
2025-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In Indonesia, the type of cancer that contributes to the highest death rate is breast cancer, so there is a great need for early examination, clinical examination, and screening, which includes mammography. Mammography is currently the most effective method for detecting early-stage breast cancer. This study aims to classify breast cancer cells based on mammogram images. The method used in this research is CNN (Convolutional Neural Network) with the NASNet Mobile model for classifying three classes: normal, benign, and malignant. The CNN method can learn various input attributes powerfully so that CNN can obtain more detailed data characteristics and has better detection capabilities. This research obtained the most optimal model based on the percentage of accuracy, sensitivity, and specificity values of 99.67%, 98.78%, and 99.35%, respectively. This research can be used to help radiologists as considerations in making breast cancer diagnosis decisions.

Keywords

Collaboration network of the authors

14 authors · 29 co-authorship links

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

  • UIN Sunan Ampel Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Unix Izyah ArfiantiUIN Sunan Ampel Surabaya1
Nanang WidodoAffiliation unknown1
Moh. HafiyusholehUIN Sunan Ampel Surabaya1
Wika Dianita UtamiUIN Sunan Ampel Surabaya1
Ghina Salsabila FirdausUIN Sunan Ampel Surabaya1
Ahmad Teguh WibowoAffiliation unknown1
Silvia Kartika SariUIN Sunan Ampel Surabaya1
Latifatun Nadya DesinainiUIN Sunan Ampel Surabaya1
Chalawatul AisUIN Sunan Ampel Surabaya1
Abdulloh HamidUIN Sunan Ampel Surabaya1

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://journal.ugm.ac.id/ijccs/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 Classification Based on Mammogram Images Using CNN Method with NASNet Mobile Model</dc:title>
	<dc:creator>Pramesti, Diah Devi</dc:creator>
	<dc:creator>Farida, Yuniar</dc:creator>
	<dc:creator>Novitasari, Dian Candra Rini</dc:creator>
	<dc:creator>Wibowo, Achmad Teguh</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science, Applied Mathematics</dc:subject>
	<dc:subject xml:lang="en-US">Breast Cancer; CNN; Deep Learning; NASNet Mobile; Mammogram Image</dc:subject>
	<dc:description xml:lang="en-US">In Indonesia, the type of cancer that contributes to the highest death rate is breast cancer, so there is a great need for early examination, clinical examination, and screening, which includes mammography. Mammography is currently the most effective method for detecting early-stage breast cancer. This study aims to classify breast cancer cells based on mammogram images. The method used in this research is CNN (Convolutional Neural Network) with the NASNet Mobile model for classifying three classes: normal, benign, and malignant. The CNN method can learn various input attributes powerfully so that CNN can obtain more detailed data characteristics and has better detection capabilities. This research obtained the most optimal model based on the percentage of accuracy, sensitivity, and specificity values of 99.67%, 98.78%, and 99.35%, respectively. This research can be used to help radiologists as considerations in making breast cancer diagnosis decisions.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/98187</dc:identifier>
	<dc:identifier>10.22146/ijccs.98187</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 3 (2025): July; 293-304</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/98187/42101</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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