A web-based pneumonia detection system using deep learning on chest X-ray image
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| Pubblicato in: | IOP Conference Series. Earth and Environmental Science vol. 1510, no. 1 (Jun 2025), p. 012054 |
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| Altri autori: | , , |
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IOP Publishing
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| 022 | |a 1755-1307 | ||
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| 024 | 7 | |a 10.1088/1755-1315/1510/1/012054 |2 doi | |
| 035 | |a 3228008937 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 100 | 1 | |a Nurdin, Y |u Department of Electrical and Computer Engineering, Universitas Syiah Kuala , Banda Aceh 23111, Indonesia | |
| 245 | 1 | |a A web-based pneumonia detection system using deep learning on chest X-ray image | |
| 260 | |b IOP Publishing |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Pneumonia is a lung infection caused by bacteria, viruses, or fungi that can cause inflammation of the air cavities in one or both lungs. Pneumonia is diagnosed using chest X-rays, but results can take 1 to 3 hours from medical experts, causing delays in treatment for most patients who have pneumonia. Therefore, a web-based artificial intelligence pneumonia detection application was developed in this study. The website development consists of several stages: requirements analysis, system design, model selection and description, application development, and testing. The deep learning model uses a convolutional neural network algorithm with VGG16, which provides effective feature extraction for complex object recognition and has two output classes. The website framework was developed using Flask with MySql for the database. The simulation results show that the classifier model accuracy rate is 98%. The web application can classify chest X-ray images with 100% accuracy on a small test set consisting of five images: two normal and three showing pneumonia. Additionally, the inference model takes only 4.09 seconds to process the classification result. Thus, our proposed e-health web-based system for pneumonia detection is accurate and real-time. | |
| 653 | |a Requirements analysis | ||
| 653 | |a Chest | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Deep learning | ||
| 653 | |a Applications programs | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Pneumonia | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Medical imaging | ||
| 653 | |a Websites | ||
| 653 | |a Systems design | ||
| 653 | |a Machine learning | ||
| 653 | |a Bronchopulmonary infection | ||
| 653 | |a Accuracy | ||
| 653 | |a X-rays | ||
| 653 | |a Lungs | ||
| 653 | |a Object recognition | ||
| 653 | |a Real time | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Haiqal, M |u Department of Electrical and Computer Engineering, Universitas Syiah Kuala , Banda Aceh 23111, Indonesia | |
| 700 | 1 | |a Azhary, M |u Faculty of Medicine, Universitas Syiah Kuala , Banda Aceh 23111, Indonesia | |
| 700 | 1 | |a Roslidar, R |u Department of Electrical and Computer Engineering, Universitas Syiah Kuala , Banda Aceh 23111, Indonesia | |
| 773 | 0 | |t IOP Conference Series. Earth and Environmental Science |g vol. 1510, no. 1 (Jun 2025), p. 012054 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3228008937/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3228008937/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |