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Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

IAES International Journal of Artificial Intelligence · Vol. 10 No. 2 (2021) · pp. 291–297

DOI
10.11591/ijai.v10.i2.pp291-297
ISSN
2252-8938, 2089-4872
Published
2021-06-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy.

Keywords

Collaboration network of the authors

40 authors · 96 co-authorship links · showing the 40 most connected

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  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ilsya WirasatiAffiliation unknown8
Jane Eva AureliaAffiliation unknown6
Glori Stephani SaragihAffiliation unknown4
Fitri HaryantiAffiliation unknown3
Lely LusmilasariAffiliation unknown3
Anang PriyantoAffiliation unknown2
Syarief Nur HusinAffiliation unknown1
Sayyida SayyidaAffiliation unknown1
Sri GunawanAffiliation unknown1
Desy Julyana SilitongaAffiliation unknown1
Adrian WinandoAffiliation unknown1
Ahmad Dzaky AttallahAffiliation unknown1
Fredica Larasti TambunanAffiliation unknown1
Mukhaira El AkmalAffiliation unknown1
Nita Permata Sari SiregarAffiliation unknown1
Beby Astri TariganAffiliation unknown1
Vilbert HerlambangAffiliation unknown1
Sally KorinAffiliation unknown1
Fillia Agaventy Br SembiringAffiliation unknown1
Karen Felycia TariganAffiliation unknown1
Jocelyn Listo GovannyAffiliation unknown1
Christine ChristineAffiliation unknown1
Rachael PatriciaAffiliation unknown1
Muhammad IkhsanInstitut Agama Islam Negeri Kendari1
Rianda ElvinawantyAffiliation unknown1
CalistaAffiliation unknown1
VeniAffiliation unknown1
Siti KunartiAffiliation unknown1
Adhitia PradanaAffiliation unknown1
Syarafina Dyah AmaliaAffiliation unknown1
Bahar ElfudllatsaniAffiliation unknown1
Hera Heru Sri SuryantiAffiliation unknown1
Ferisa Prasetyaning UtamiAffiliation unknown1
Muhammad Pramono HadiAffiliation unknown1
S SudibyaktoAffiliation unknown1
Aris PonimanAffiliation unknown1

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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (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">Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image</dc:title>
	<dc:creator>Rustam, Zuherman</dc:creator>
	<dc:creator>Purwanto, Aldi</dc:creator>
	<dc:creator>Hartini, Sri</dc:creator>
	<dc:creator>Saragih, Glori Stephani</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Fuzzy c-means; Fuzzy kernel c-means; Image classification; Lung nodule; Machine learning</dc:subject>
	<dc:description xml:lang="en-US">Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </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>2021-06-01</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/20845</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i2.pp291-297</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 2: June 2021; 291-297</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20845/13101</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 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>