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Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network

IAES International Journal of Artificial Intelligence · Vol. 11 No. 4 (2022) · hlm. 1306–1313

DOI
10.11591/ijai.v11.i4.pp1306-1313
ISSN
2252-8938, 2089-4872
Terbit
2022-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Glioma as one of the most common types of brain tumor in the world has three different classes based on its cell types. They are astrocytoma, ependymoma, oligodendroglioma, each has different characteristics depending on the location and malignance level. Radiological examination by medical personnel is still carried out manually using magnetic resonance imaging (MRI) medical imaging. Brain structure, size, and various forms of tumors increase the level of difficulty in classifying gliomas. It is advisable to apply a method that can conduct gliomas classification through medical images. The proposed methods were proposed for this study using deep convolutional neural network (DCNN) for classification with k-means segmentation and contrast enhancement. The results show the effectiveness of the proposed methods with an accuracy of 95.5%.

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  • Universitas Sumatera Utara
  • Politeknik Negeri Medan
  • International Islamic University Malaysia
  • Telkom University
  • Bandung Institute of Technology
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  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Arif Ridho LubisAfiliasi tidak diketahui4
Michael J. WattsAfiliasi tidak diketahui4
Romi Fadillah RahmatAfiliasi tidak diketahui3
Julham JulhamAfiliasi tidak diketahui3
Idham KamilAfiliasi tidak diketahui3
Yuyun Yusnida LasePoliteknik Negeri Medan2
Syahril EfendiUniversitas Sumatera Utara2
Elviawaty Muisa ZamzamiUniversitas Sumatera Utara2
Opim Salim SitompulUniversitas Sumatera Utara2
Mahyuddin K. M. NasutionUniversitas Sumatera Utara2
Mahyuddin Khairuddin Matyuso NasutionAfiliasi tidak diketahui2
Dewi WulandariAfiliasi tidak diketahui1
Lilis Tiara AdhaPoliteknik Negeri Medan1
Tika AriyaniPoliteknik Negeri Medan1
Fahdi Saidi LubisInternational Islamic University Malaysia1
Hanif FakhrurrojaTelkom University1
Rian Bimo AnkhalAfiliasi tidak diketahui1
Muhammad Fakhrul SafitraAfiliasi tidak diketahui1
Yulio FerdinandAfiliasi tidak diketahui1
Oktariani Nurul PratiwiAfiliasi tidak diketahui1
Rio Guntur UtomoAfiliasi tidak diketahui1
Jonathan Arya WibowoAfiliasi tidak diketahui1
Kobthong LadkoomAfiliasi tidak diketahui1
Sinung SuakantoBandung Institute of Technology1
Joko SiswantoAfiliasi tidak diketahui1
Jan M. PawlowskiAfiliasi tidak diketahui1
Syfa Nur LathifahAfiliasi tidak diketahui1
Litasari Widyastuti SuwarsonoAfiliasi tidak diketahui1
Hariyadi HariyadiAfiliasi tidak diketahui1
Ferry FachrizalAfiliasi tidak diketahui1
Amrullah AmrullahAfiliasi tidak diketahui1
Akbar IdamanAfiliasi tidak diketahui1
Herman MawengkangAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Astrocytoma, ependymoma, and oligodendroglioma  classification with deep convolutional neural network</dc:title>
	<dc:creator>Rahmat, Romi Fadillah</dc:creator>
	<dc:creator>Pratama, Mhd Faris</dc:creator>
	<dc:creator>Purnamawati, Sarah</dc:creator>
	<dc:creator>Faza, Sharfina</dc:creator>
	<dc:creator>Lubis, Arif Ridho</dc:creator>
	<dc:creator>Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:creator>Lubis, Muharman</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Brain tumor; Deep convolutional neural  network; Glioma classification;</dc:subject>
	<dc:description xml:lang="en-US">Glioma as one of the most common types of brain tumor in the world has three different classes based on its cell types. They are astrocytoma, ependymoma, oligodendroglioma, each has different characteristics depending on the location and malignance level. Radiological examination by medical personnel is still carried out manually using magnetic resonance imaging (MRI) medical imaging. Brain structure, size, and various forms of tumors increase the level of difficulty in classifying gliomas. It is advisable to apply a method that can conduct gliomas classification through medical images. The proposed methods were proposed for this study using deep convolutional neural network (DCNN) for classification with k-means segmentation and contrast enhancement. The results show the effectiveness of the proposed methods with an accuracy of 95.5%.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21882</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i4.pp1306-1313</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 4: December 2022; 1306-1313</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21882/13468</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>