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

Ahmad Rafiansyah Fauzan, Ahmad Rafiansyah Fauzan and Mohammad Iwan Wahyuddin, Mohammad Iwan Wahyuddin and Sari Ningsih, Sari Ningsih (2021) Pleural Effusion Classification Based on Chest X-Ray Images using Convolutional Neural Network. Jurnal Ilmu Komputer dan Informasi, 14 (1). pp. 9-15. ISSN e-ISSN 2502-9274

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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.

Item Type:	Article
Uncontrolled Keywords:	Computer Vision; Image Classification; Convolutional Neural Network; Pleural Effusion
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Ni Made Yunia Dwi Savitri
Date Deposited:	17 Nov 2022 01:35
Last Modified:	17 Nov 2022 01:35
URI:	http://eprints.triatmamulya.ac.id/id/eprint/1833

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