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

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

Tags

,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon ,

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.