COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia
Introduction: Every area of our lives has been devastated by the worldwide Coronavirus disease 2019 (COVID-19) epidemic. However, the development of artificial intelligence has made it possible to build advanced applications that can fulfill this level of clinical accuracy. This study aimed to creat...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Airlangga
2023-01-01
|
| Series: | Jurnal Respirasi |
| Subjects: | |
| Online Access: | https://e-journal.unair.ac.id/JR/article/view/39787 |
| _version_ | 1811169488134995968 |
|---|---|
| author | Budi Yanti Yudha Nurdin Teuku Geumpana |
| author_facet | Budi Yanti Yudha Nurdin Teuku Geumpana |
| author_sort | Budi Yanti |
| collection | DOAJ |
| description | Introduction: Every area of our lives has been devastated by the worldwide Coronavirus disease 2019 (COVID-19) epidemic. However, the development of artificial intelligence has made it possible to build advanced applications that can fulfill this level of clinical accuracy. This study aimed to create a deep learning model that can detect COVID-19 from a chest image dataset of confirmed patients treated at the provincial hospital in Aceh.
Methods: Eight hundred confirmed COVID-19 patients' chest X-ray photos were gathered locally from Dr. Zainoel Abidin General Hospital, Banda Aceh. Performance was evaluated in several ways. First, the dataset was used for training and testing. Second, the data was used to train and test the model. VGG16 is a robust network adapted to an enhanced dataset constructed from a confirmed COVID-19 chest X-ray pool. To artificially produce a huge number of chest X-ray pictures, this study used data augmentation techniques such as random rotation at an angle between 10 and 10°, random noise, and horizontal flips.
Results: The experimental results were encouraging: the proposed models classified chest X-ray pictures as normal or COVID-19 with an accuracy of 97.20% for Resnet50, 98.10% for InceptionV3, and 98.30% for VGG16. The results showed the outstanding performance of straightforward COVID-19 diagnosis with the classification of COVID-19 severity, such as mild, severe, and very severe.
Conclusion: These made it possible to automate the X-ray image interpretation process accurately and could also be applied when materials and reverse transcription polymerase chain reaction (RT-PCR) tests are scarce. |
| first_indexed | 2024-04-10T16:44:20Z |
| format | Article |
| id | doaj.art-654fdefb78d94e4f8fad730ff30ebedc |
| institution | Directory Open Access Journal |
| issn | 2407-0831 2621-8372 |
| language | English |
| last_indexed | 2024-04-10T16:44:20Z |
| publishDate | 2023-01-01 |
| publisher | Universitas Airlangga |
| record_format | Article |
| series | Jurnal Respirasi |
| spelling | doaj.art-654fdefb78d94e4f8fad730ff30ebedc2023-02-08T03:37:22ZengUniversitas AirlanggaJurnal Respirasi2407-08312621-83722023-01-01913036https://doi.org/10.20473/jr.v9-I.1.2023.30-36COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, IndonesiaBudi Yanti0https://orcid.org/0000-0003-2932-0764Yudha Nurdin1https://orcid.org/0000-0001-7117-5850Teuku Geumpana2https://orcid.org/0000-0003-0307-0912 Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia.Faculty of Electrical Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia.School of Information and Physical Sciences, College of Engineering, Science, and Environment, The University of Newcastle, Newcastle, Australia.Introduction: Every area of our lives has been devastated by the worldwide Coronavirus disease 2019 (COVID-19) epidemic. However, the development of artificial intelligence has made it possible to build advanced applications that can fulfill this level of clinical accuracy. This study aimed to create a deep learning model that can detect COVID-19 from a chest image dataset of confirmed patients treated at the provincial hospital in Aceh. Methods: Eight hundred confirmed COVID-19 patients' chest X-ray photos were gathered locally from Dr. Zainoel Abidin General Hospital, Banda Aceh. Performance was evaluated in several ways. First, the dataset was used for training and testing. Second, the data was used to train and test the model. VGG16 is a robust network adapted to an enhanced dataset constructed from a confirmed COVID-19 chest X-ray pool. To artificially produce a huge number of chest X-ray pictures, this study used data augmentation techniques such as random rotation at an angle between 10 and 10°, random noise, and horizontal flips. Results: The experimental results were encouraging: the proposed models classified chest X-ray pictures as normal or COVID-19 with an accuracy of 97.20% for Resnet50, 98.10% for InceptionV3, and 98.30% for VGG16. The results showed the outstanding performance of straightforward COVID-19 diagnosis with the classification of COVID-19 severity, such as mild, severe, and very severe. Conclusion: These made it possible to automate the X-ray image interpretation process accurately and could also be applied when materials and reverse transcription polymerase chain reaction (RT-PCR) tests are scarce.https://e-journal.unair.ac.id/JR/article/view/39787covid-19chest x-rayconvolutional neural networkdeep learninginfectious disease |
| spellingShingle | Budi Yanti Yudha Nurdin Teuku Geumpana COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia Jurnal Respirasi covid-19 chest x-ray convolutional neural network deep learning infectious disease |
| title | COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia |
| title_full | COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia |
| title_fullStr | COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia |
| title_full_unstemmed | COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia |
| title_short | COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia |
| title_sort | covid 19 severity based on deep convolutional neural networks chest x ray image in aceh indonesia |
| topic | covid-19 chest x-ray convolutional neural network deep learning infectious disease |
| url | https://e-journal.unair.ac.id/JR/article/view/39787 |
| work_keys_str_mv | AT budiyanti covid19severitybasedondeepconvolutionalneuralnetworkschestxrayimageinacehindonesia AT yudhanurdin covid19severitybasedondeepconvolutionalneuralnetworkschestxrayimageinacehindonesia AT teukugeumpana covid19severitybasedondeepconvolutionalneuralnetworkschestxrayimageinacehindonesia |