TELKOMNIKA Telecommunication, Computing, Electronics and Control
Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks
Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks
Subject
Classification, CNNs, Pneumonia, X-ray
Description
Every organism is known to have different structural and biological system, specifically in human immunity. If the immune system weakens, the body is susceptible to disease especially pneumonia disease. Pneumonia disease is caused by the bacterium Streptococcus pneumonia, and according to the World Health Organization (WHO), it is identified as the leading cause of death in children worldwide, which is about 16%, for those under the age of 5. Meanwhile, someone who is predicted to have pneumonia by a doctor is recommended for an X-ray. Convolutional neural networks (CNNs) is an accurate method to help the doctor's predicted correctly. CNNs is divided into two important parts, feature extraction layer (convolutional layer and pooling layer) and fully connected layer. CNNs method is commonly used for image data classification. Therefore, CNNs is suitable to classify pneumonia based on lung X-ray in order to obtain accurate prediction results. And then, the results can be seen based on the graph of the accuracy value and the loss value. When CNNs method applied on the dataset, an accuracy rate of 97% was obtained. Based on accuracy rate, it shows that CNNs can be applied to image data (especially lung X-ray) for classification of pneumonia disease.
Creator
Zuherman Rustam, Rivan Pratama Yuda, Hamimah Alatas, Chelvian Aroef
Source
DOI: 10.12928/TELKOMNIKA.v18i3.14839
Publisher
Universitas Ahmad Dahlan
Date
June 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Language
English
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Zuherman Rustam, Rivan Pratama Yuda, Hamimah Alatas, Chelvian Aroef, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks,” Repository Horizon University Indonesia, accessed November 13, 2024, https://repository.horizon.ac.id/items/show/3868.
Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks,” Repository Horizon University Indonesia, accessed November 13, 2024, https://repository.horizon.ac.id/items/show/3868.