Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network
Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this st...
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Institute of Advanced Engineering and Science
2024
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2-s2.0-85200670121 Cahyani D.E.; Hariadi A.D.; Setyawan F.F.; Gumilar L.; Setumin S. Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network 2024 Bulletin of Electrical Engineering and Informatics 13 5 10.11591/eei.v13i5.7825 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200670121&doi=10.11591%2feei.v13i5.7825&partnerID=40&md5=851557f0a85916329b18ee379a58ce04 Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies. © 2024, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 20893191 English Article All Open Access; Gold Open Access |
author |
Cahyani D.E.; Hariadi A.D.; Setyawan F.F.; Gumilar L.; Setumin S. |
spellingShingle |
Cahyani D.E.; Hariadi A.D.; Setyawan F.F.; Gumilar L.; Setumin S. Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
author_facet |
Cahyani D.E.; Hariadi A.D.; Setyawan F.F.; Gumilar L.; Setumin S. |
author_sort |
Cahyani D.E.; Hariadi A.D.; Setyawan F.F.; Gumilar L.; Setumin S. |
title |
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
title_short |
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
title_full |
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
title_fullStr |
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
title_full_unstemmed |
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
title_sort |
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network |
publishDate |
2024 |
container_title |
Bulletin of Electrical Engineering and Informatics |
container_volume |
13 |
container_issue |
5 |
doi_str_mv |
10.11591/eei.v13i5.7825 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200670121&doi=10.11591%2feei.v13i5.7825&partnerID=40&md5=851557f0a85916329b18ee379a58ce04 |
description |
Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies. © 2024, Institute of Advanced Engineering and Science. All rights reserved. |
publisher |
Institute of Advanced Engineering and Science |
issn |
20893191 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1818940550863126528 |