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Texture feature extraction for the lung lesion density classification on computed tomography scan image

Communications in Science and Technology · Vol. 1 No. 1 (2016)

DOI
10.21924/cst.1.1.2016.14
ISSN
2502-9266, 2502-9258
Terbit
2016-05-31
Penerbit
Komunitas Ilmuwan dan Profesional Muslim Indonesia
Bahasa
eng

Abstrak

The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to minimize the mortality rate. However, the assessment and diagnosis by an expert are subjective depending on the competence and experience of a radiologist. Hence, a digital image processing of CT scan is necessary as a tool to diagnose the lung cancer. This research proposes a morphological characteristics method for detecting lung cancer lesion density by using the histogram and GLCM (Gray Level Co-occurrence Matrices). The most well-known artificial neural network (ANN) architecture that is the multilayers perceptron (MLP), is used in classifying lung cancer lesion density of heterogeneous and homogeneous. Fifty CT scan images of lungs obtained from the Department of Radiology of RSUP Dr. Sardjito Hospital, Yogyakarta are used as the database. The results show that the proposed method achieved the accuracy of 98%, sensitivity of 96%, and specificity of 96%.

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33 penulis · 76 tautan kepenulisan bersama · 22 lintas organisasi

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  • Universitas Gadjah Mada
  • Telkom University
  • Universitas Islam Negeri Imam Bonjol Padang
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Sunan Gunung Djati State Islamic University Bandung
  • State University of Semarang
  • Institut Teknologi Telkom Purwokerto
  • Organisasi lain
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Noor Akhmad SetiawanUniversitas Gadjah Mada5
Igi ArdiyantoUniversitas Gadjah Mada3
Widhia K.Z. OktoeberzaUniversitas Gadjah Mada3
Achmad RizalTelkom University2
Risanuri HidayatUniversitas Gadjah Mada2
Indah SoesantiUniversitas Gadjah Mada2
Aulia ArhamUniversitas Islam Negeri Imam Bonjol Padang2
Anan NugrohoState University of Semarang2
Latifah ListyalinaUniversitas Gadjah Mada2
Faza Maula AzifUniversitas Gadjah Mada2
Shofwatul UyunSunan Kalijaga State Islamic University Yogyakarta1
Willy Anugrah CahyadiTelkom University1
Tri Putri LestariSunan Gunung Djati State Islamic University Bandung1
Rezty Amalia ArasUniversitas Gadjah Mada1
Christianus Frederick HotamaUniversitas Gadjah Mada1
Oyas WahyunggoroUniversitas Gadjah Mada1
Herryawan PujiharsonoInstitut Teknologi Telkom Purwokerto1
Febry Putra RochimUniversitas Gadjah Mada1
M. Azka PutraUniversitas Gadjah Mada1
Chandra Ramadhan Atmaja PerdanaUniversitas Gadjah Mada1
Suatmi MurnaniIslamic University of Indonesia1
Dian Nova Kusuma HardaniMuhammadiyah University Purwokerto1
Aqil AqthobirrobbanyUniversitas Gadjah Mada1
Syukron Abu Ishaq AlfaroziUniversitas Gadjah Mada1
Etik IrijantiMuhammadiyah University of Yogyakarta1
Teguh Bharata AdjiUniversitas Gadjah Mada1
Fauziazzuhry RahadianUniversitas Gadjah Mada1
Ratna Lestari Budiani BuanaUniversitas Gadjah Mada1

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Sumber metadata: OAI-PMH (metadata jurnal) https://cst.kipmi.or.id/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Texture feature extraction for the lung lesion density classification on computed tomography scan image</dc:title>
	<dc:creator>Hasnely, Hasnely</dc:creator>
	<dc:creator>Nugroho, Hanung Adi</dc:creator>
	<dc:creator>Wibirama, Sunu</dc:creator>
	<dc:creator>Windarta, Budi</dc:creator>
	<dc:creator>Choridah, Lina</dc:creator>
	<dc:description xml:lang="en-US">The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to minimize the mortality rate. However, the assessment and diagnosis by an expert are subjective depending on the competence and experience of a radiologist. Hence, a digital image processing of CT scan is necessary as a tool to diagnose the lung cancer. This research proposes a morphological characteristics method for detecting lung cancer lesion density by using the histogram and GLCM (Gray Level Co-occurrence Matrices). The most well-known artificial neural network (ANN) architecture that is the multilayers perceptron (MLP), is used in classifying lung cancer lesion density of heterogeneous and homogeneous. Fifty CT scan images of lungs obtained from the Department of Radiology of RSUP Dr. Sardjito Hospital, Yogyakarta are used as the database. The results show that the proposed method achieved the accuracy of 98%, sensitivity of 96%, and specificity of 96%. </dc:description>
	<dc:publisher xml:lang="en-US">Komunitas Ilmuwan dan Profesional Muslim Indonesia</dc:publisher>
	<dc:date>2016-05-31</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:identifier>https://cst.kipmi.or.id/journal/article/view/14</dc:identifier>
	<dc:identifier>10.21924/cst.1.1.2016.14</dc:identifier>
	<dc:source xml:lang="en-US">Communications in Science and Technology; Vol. 1 No. 1 (2016)</dc:source>
	<dc:source xml:lang="id-ID">Communications in Science and Technology; Vol 1 No 1 (2016)</dc:source>
	<dc:source>2502-9266</dc:source>
	<dc:source>2502-9258</dc:source>
	<dc:source>10.21924/cst.1.1.2016</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://cst.kipmi.or.id/journal/article/view/14/6</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 Communications in Science and Technology</dc:rights>
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