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A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks

Advance Sustainable Science, Engineering and Technology (ASSET) · Vol. 6 No. 1 (2023)

DOI
10.26877/asset.v6i1.17330
ISSN
2715-4211, 2715-4211
Terbit
2023-12-10
Penerbit
Universitas PGRI Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

CNN has been widely used to detect a pattern with image classification. This study used CNN to perform a classification analysis of lung abnormality detection on chest X-ray images. The dataset consists of 5,732 2D images with dimensions of 200 x 200 x 1 divided into training data (85%) and testing data (15%). The preprocessing process includes image resizing, enhancement to increase contrast and reduce image complexity, and filtering to improve visibility and reduce noise. CNN is used to classify imagery into three categories, Normal (no abnormalities), Pneumonia, and Tuberculosis. The results showed a good level of accuracy, with an average accuracy of 97.24% in 3 trainings, and a 100% success rate in 6 classification experiments. This research provides insights into the detection of lung disorders and encourages further exploration in medical diagnosis.

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  • Universitas Dian Nuswantoro
  • Institut Teknologi Sepuluh Nopember
  • University of Hartford
  • MathWorks (United States)
  • Universiti Teknikal Malaysia Melaka
  • University of Technology Malaysia
  • Northern Technical University
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Rekan penulisOrganisasiArtikel bersama
De Rosal Ignatius Moses SetiadiUniversitas Dian Nuswantoro6
Ajib SusantoUniversitas Dian Nuswantoro5
Ibnu Utomo Wahyu MulyonoUniversitas Dian Nuswantoro5
Edi Jaya KusumaUniversitas Dian Nuswantoro4
Md Kamruzzaman SarkerUniversity of Hartford4
Hussain Md Mehedul IslamMathWorks (United States)3
Mohamed DoheirUniversiti Teknikal Malaysia Melaka3
Ega Adiasa SofyanUniversitas Dian Nuswantoro3
Maulana Malik Ibrahim Al-GhiffaryUniversitas Dian Nuswantoro3
Noorayisahbe Mohd YacoobUniversity of Technology Malaysia3
Rabei Raad AliNorthern Technical University3
Fadillah FarhanUniversitas Dian Nuswantoro3
Abdussalam AbdussalamUniversitas Dian Nuswantoro2
Ivan StephengUniversitas Dian Nuswantoro2
Folasade Olubusola IsinkayeEkiti State University2
Ervira Aliya NabilaUniversitas Dian Nuswantoro2
Anny YuniartiInstitut Teknologi Sepuluh Nopember1
Rizky Damara ArdyInstitut Teknologi Sepuluh Nopember1
Yupie KusumawatiUniversitas Dian Nuswantoro1
Elkaf Rahmawan PramudyaUniversitas Dian Nuswantoro1
Zudha PratamaUniversitas Dian Nuswantoro1

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Sumber metadata: OAI-PMH (metadata jurnal) http://journal.upgris.ac.id/index.php/asset/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">A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks</dc:title>
	<dc:creator>Kamila, Izza Putri</dc:creator>
	<dc:creator>Sari, Christy Atika</dc:creator>
	<dc:creator>Rachmawanto, Eko Hari</dc:creator>
	<dc:creator>Cahyo, Nur Ryan Dwi</dc:creator>
	<dc:subject xml:lang="en-US">Pneumonia; Tuberculosis; TBC; CNN; Image Classification</dc:subject>
	<dc:description xml:lang="en-US">CNN has been widely used to detect a pattern with image classification. This study used CNN to perform a classification analysis of lung abnormality detection on chest X-ray images. The dataset consists of 5,732 2D images with dimensions of 200 x 200 x 1 divided into training data (85%) and testing data (15%). The preprocessing process includes image resizing, enhancement to increase contrast and reduce image complexity, and filtering to improve visibility and reduce noise. CNN is used to classify imagery into three categories, Normal (no abnormalities), Pneumonia, and Tuberculosis. The results showed a good level of accuracy, with an average accuracy of 97.24% in 3 trainings, and a 100% success rate in 6 classification experiments. This research provides insights into the detection of lung disorders and encourages further exploration in medical diagnosis.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas PGRI Semarang</dc:publisher>
	<dc:contributor xml:lang="en-US">Magister Pendidikan Fisika, FKIP Universitas Sriwijaya</dc:contributor>
	<dc:date>2023-12-10</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.upgris.ac.id/index.php/asset/article/view/17330</dc:identifier>
	<dc:identifier>10.26877/asset.v6i1.17330</dc:identifier>
	<dc:source xml:lang="en-US">Advance Sustainable Science, Engineering and Technology; Vol 6, No 1 (2024): November-January; 0240102</dc:source>
	<dc:source>2715-4211</dc:source>
	<dc:source>2715-4211</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.upgris.ac.id/index.php/asset/article/view/17330/pdf</dc:relation>
	<dc:relation>https://journal.upgris.ac.id/index.php/asset/article/downloadSuppFile/17330/3735</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Advance Sustainable Science Engineering and Technology</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>