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Pleural Effusion Classification Based on Chest X-Ray Images using Convolutional Neural Network

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 14 No. 1 (2021) · pp. 9–16

DOI
10.21609/jiki.v14i1.898
ISSN
2502-9274, 2088-7051
Published
2021-02-28
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Pleural effusion is a respiratory infection characterized by a buildup of fluid between the two layers of pleura, which causes specific symptoms such as chest pain and shortness of breath. In Indonesia, pleural effusion cases alone account for 2.7% of other respiratory infections, with an estimated number of sufferers in general at more than 3000 people per 1 million population annually. Pleural effusion is a severe case and can cause death if not treated immediately. Based on a study, as many as 15% of 104 patients diagnosed with pleural effusion died within 30 days. In this paper, we present a model that can detect pleural effusion based on chest x-ray images automatically using a Machine Learning algorithm. The machine learning algorithm used is Convolutional Neural Network (CNN), with the dataset used from ChestX-ray14. The number of data used was 2500 in the form of x-ray images, based on two different classes, x-ray with pleural effusion and x-ray with normal condition. The evaluation result shows that the CNN model can classify data with an accuracy of 95% of the test set data; thus, we hope it can be an alternative to assist medical diagnosis in pleural effusion detection.

Keywords

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Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Pleural Effusion Classification Based on Chest X-Ray Images using Convolutional Neural Network</dc:title>
	<dc:creator>Fauzan, Ahmad Rafiansyah</dc:creator>
	<dc:creator>Wahyuddin, Mohammad Iwan</dc:creator>
	<dc:creator>Ningsih, Sari</dc:creator>
	<dc:subject xml:lang="en-US">Computer Vision</dc:subject>
	<dc:subject xml:lang="en-US">Image Classification</dc:subject>
	<dc:subject xml:lang="en-US">Convolutional Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Pleural Effusion</dc:subject>
	<dc:description xml:lang="en-US">Pleural effusion is a respiratory infection characterized by a buildup of fluid between the two layers of pleura, which causes specific symptoms such as chest pain and shortness of breath. In Indonesia, pleural effusion cases alone account for 2.7% of other respiratory infections, with an estimated number of sufferers in general at more than 3000 people per 1 million population annually. Pleural effusion is a severe case and can cause death if not treated immediately. Based on a study, as many as 15% of 104 patients diagnosed with pleural effusion died within 30 days. In this paper, we present a model that can detect pleural effusion based on chest x-ray images automatically using a Machine Learning algorithm. The machine learning algorithm used is Convolutional Neural Network (CNN), with the dataset used from ChestX-ray14. The number of data used was 2500 in the form of x-ray images, based on two different classes, x-ray with pleural effusion and x-ray with normal condition. The evaluation result shows that the CNN model can classify data with an accuracy of 95% of the test set data; thus, we hope it can be an alternative to assist medical diagnosis in pleural effusion detection.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2021-02-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/898</dc:identifier>
	<dc:identifier>10.21609/jiki.v14i1.898</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 14 No. 1 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 9-16</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v14i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/898/437</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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