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

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

Computer Vision, Image Classification, Convolutional Neural Network, Pleural Effusion

Pleural effusion is an abnormal lung condition characterized by a buildup of fluid between the two layers of the 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 diseases, 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. This paper proposes a model that automatically detects pleural effusion based on chest x-ray images 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 xray 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.

Ahmad Rafiansyah Fauzan, Mohammad Iwan Wahyuddin, and Sari Ningsih

http://dx.doi.org/10.21609/jiki.v14i1.898

Faculty of Computer Science Universitas Indonesia

2021-02-28

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)