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Brain Tumors Detection By Using Convolutional Neural Networks and Selection of Thresholds By Histogram Selection

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 14 No. 2 (2021) · pp. 83–89

DOI
10.21609/jiki.v14i2.859
ISSN
2502-9274, 2088-7051
Published
2021-07-04
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Brain tumors in medical images have a high diversity in terms of shape and size. Some of the data found a form between the tumor tissue and normal tissue, whereas knowing the tumor’s profile and characteristics becomes a crucial part of searching. By using machine learning capabilities, where machines are given several variables and provide decisions to a certain degree, they have broadly given decisions that support subject matter in making decisions. This study applies the threshold selection method using histogram selection on CT scan data, while the appropriate threshold selection method selects the tumor position accordingly. Furthermore, the Convolutional Neural Network (CNN) is used to classify whether the selected image is a tumor or not. Using CT scan data and calculated experiments, this algorithm can finally be approved and given a brain classification with an accuracy of 75.42 percent.

Keywords

Collaboration network of the authors

2 authors · 1 co-authorship link

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  • Universitas Nusa Mandiri
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  • Same organisation
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Co-authorOrganisationJoint articles
Mila Desi AnasantiUniversitas Nusa Mandiri1

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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Brain Tumors Detection By Using Convolutional Neural Networks and Selection of Thresholds By Histogram Selection</dc:title>
	<dc:creator>Aprianto, Kasiful</dc:creator>
	<dc:subject xml:lang="en-US">Convolutional Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Histogram Selection</dc:subject>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:description xml:lang="en-US">Brain tumors in medical images have a high diversity in terms of shape and size. Some of the data found a form between the tumor tissue and normal tissue, whereas knowing the tumor’s profile and characteristics becomes a crucial part of searching. By using machine learning capabilities, where machines are given several variables and provide decisions to a certain degree, they have broadly given decisions that support subject matter in making decisions. This study applies the threshold selection method using histogram selection on CT scan data, while the appropriate threshold selection method selects the tumor position accordingly. Furthermore, the Convolutional Neural Network (CNN) is used to classify whether the selected image is a tumor or not. Using CT scan data and calculated experiments, this algorithm can finally be approved and given a brain classification with an accuracy of 75.42 percent.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2021-07-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:format>application/zip</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/859</dc:identifier>
	<dc:identifier>10.21609/jiki.v14i2.859</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 14 No. 2 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 83-89</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v14i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/859/445</dc:relation>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/859/476</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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