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Classification of skin cancer images by applying simple evolving connectionist system

IAES International Journal of Artificial Intelligence · Vol. 10 No. 2 (2021) · pp. 421–429

DOI
10.11591/ijai.v10.i2.pp421-429
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

Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.

Keywords

Collaboration network of the authors

27 authors · 77 co-authorship links

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

  • Universitas Sumatera Utara
  • Universitas Halu Oleo
  • Universitas Muhammadiyah Sumatera Utara
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arif Ridho LubisAffiliation unknown4
Muharman LubisAffiliation unknown4
Romi Fadillah RahmatAffiliation unknown2
Arif Ridho LubisAffiliation unknown2
Syahril EfendiUniversitas Sumatera Utara2
Michael J. WattsAffiliation unknown2
Romi Fadillah RahmatAffiliation unknown2
Julham JulhamAffiliation unknown1
Idham KamilAffiliation unknown1
Hariyadi HariyadiAffiliation unknown1
Ferry FachrizalAffiliation unknown1
Amrullah AmrullahAffiliation unknown1
Akbar IdamanAffiliation unknown1
Mahyuddin Khairuddin Matyuso NasutionAffiliation unknown1
Herman MawengkangAffiliation unknown1
Anton Abdulbasah KamilAffiliation unknown1
Sarah PurnamawatiAffiliation unknown1
Mhd Faris PratamaAffiliation unknown1
Sharfina FazaAffiliation unknown1
Irvan IrvanUniversitas Halu Oleo1
Habibi Ramdani SafitriAffiliation unknown1
Muhammad BasriAffiliation unknown1
Akrim AkrimUniversitas Muhammadiyah Sumatera Utara1
Fatma Sari HutagalungAffiliation unknown1
Halim MaulanaAffiliation 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) 
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	<dc:title xml:lang="en-US">Classification of skin cancer images by applying simple evolving connectionist system</dc:title>
	<dc:creator>Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:creator>Suherman, Suherman</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Classification; Data mining; SECoS; Skin care</dc:subject>
	<dc:description xml:lang="en-US">Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.</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/20838</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i2.pp421-429</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 2: June 2021; 421-429</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/20838/13123</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>