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Evaluation of psoriasis skin disease classification using convolutional neural network

IAES International Journal of Artificial Intelligence · Vol. 9 No. 2 (2020) · hlm. 349–355

DOI
10.11591/ijai.v9.i2.pp349-355
ISSN
2252-8938, 2089-4872
Terbit
2020-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Skin disease has lower impact on mortality compared to others but instead it has greater effect on quality of life because it involves symptoms such as pain, stinging and itchiness. Psoriasis is one of the ordinary skin diseases which are relapsing, chronic and immune-mediated inflammatory disease. It is estimated about 125 million people worldwide being infected with various types of skin infection. Challenges arise when patients only predict the skin type disease they had without being accurately and precisely examined. This is because as human being, they only observe and look at the diseases on the surface of the skin with their naked eye, where there are some limits, for example, human vision lacks of accuracy, reproducibility and quantification in the collection of image information. As Plaque and Guttate are the most common Psoriasis skin disease happened among people, this paper presents an evaluation of Psoriasis skin disease classification using Convolutional Neural Network. A total of 187 images which consist of 82 images for Plaque Psoriasis and 105 images for Guttate Psoriasis has been used which are retrieved from Psoriasis Image Library, International Psoriasis Council (IPC) and DermNet NZ. Convolutional Neural Network (CNN) is applied in extracting features and analysing the classification of Psoriasis skin disease. This paper showed the promising used of CNN with the accuracy rate of 82.9% and 72.4% for Plaque and Guttate Psoriasis skin disease, respectively.

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21 penulis · 56 tautan kepenulisan bersama · 4 lintas organisasi

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  • Universiti Teknologi MARA
  • Indonesia University of Education
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  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Shafaf IbrahimUniversiti Teknologi MARA6
Nur Nabilah Abu MangshorUniversiti Teknologi MARA4
Nurul Shafekah KassimUniversiti Teknologi MARA3
Norizan Mat DiahUniversiti Teknologi MARA2
Lala Septem RizaIndonesia University of Education1
Raseeda HamzahUniversiti Teknologi MARA1
Amni Munira KhidirUniversiti Teknologi MARA1
Nurul Amirah ZulkifliUniversiti Teknologi MARA1
Anis Amilah ShariUniversiti Teknologi MARA1
Mohd Rahmat Mohd NoordinUniversiti Teknologi MARA1
Iylia Ashiqin Abdul MajidUniversiti Teknologi MARA1
Danishah Hana Muhammad Muhaimin LimUniversiti Teknologi MARA1
Zolidah KasiranUniversiti Teknologi MARA1
Muhammad Fareez RashdanUniversiti Teknologi MARA1
Nor Masri SahriUniversiti Teknologi MARA1
Nursuriati JamilUniversiti Teknologi MARA1
Gehad Saleh Ahmed MohammedUniversiti Teknologi MARA1

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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">Evaluation of psoriasis skin disease classification using convolutional neural network</dc:title>
	<dc:creator>binti Roslan, Rosniza</dc:creator>
	<dc:creator>Mohd Razly, Iman Najwa</dc:creator>
	<dc:creator>Sabri, Nurbaity</dc:creator>
	<dc:creator>Ibrahim, Zaidah</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Classification, Convolutional neural network, Deep learning, Psoriasis, Skin disease</dc:subject>
	<dc:description xml:lang="en-US">Skin disease has lower impact on mortality compared to others but instead it has greater effect on quality of life because it involves symptoms such as pain, stinging and itchiness.  Psoriasis is one of the ordinary skin diseases which are relapsing, chronic and immune-mediated inflammatory disease.  It is estimated about 125 million people worldwide being infected with various types of skin infection.  Challenges arise when patients only predict the skin type disease they had without being accurately and precisely examined.  This is because as human being, they only observe and look at the diseases on the surface of the skin with their naked eye, where there are some limits, for example, human vision lacks of accuracy, reproducibility and quantification in the collection of image information.  As Plaque and Guttate are the most common Psoriasis skin disease happened among people, this paper presents an evaluation of Psoriasis skin disease classification using Convolutional Neural Network.  A total of 187 images which consist of 82 images for Plaque Psoriasis and 105 images for Guttate Psoriasis has been used which are retrieved from Psoriasis Image Library, International Psoriasis Council (IPC) and DermNet NZ.  Convolutional Neural Network (CNN) is applied in extracting features and analysing the classification of Psoriasis skin disease.  This paper showed the promising used of CNN with the accuracy rate of 82.9% and 72.4% for Plaque and Guttate Psoriasis skin disease, respectively.</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>2020-06-01</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/20389</dc:identifier>
	<dc:identifier>10.11591/ijai.v9.i2.pp349-355</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 9, No 2: June 2020; 349-355</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v9.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20389/13038</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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>