LUNGINFORMER: A Multiclass of lung pneumonia diseases detection based on chest X-ray image using contrast enhancement and hybridization inceptionresnet and transformer
Lung pneumonia is categorically a serious disease on Earth. In December 2019, COVID-19 was first identified in Wuhan, China. COVID-19 caused severe lung pneumonia. The majority of lung pneumonia diseases are diagnosed using traditional medical tools and specialized medical personnel. This process is...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2025-05-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | https://ijain.org/index.php/IJAIN/article/view/1964 |
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| Summary: | Lung pneumonia is categorically a serious disease on Earth. In December 2019, COVID-19 was first identified in Wuhan, China. COVID-19 caused severe lung pneumonia. The majority of lung pneumonia diseases are diagnosed using traditional medical tools and specialized medical personnel. This process is both time-consuming and expensive. To address the problem, many researchers have employed deep learning algorithms to develop an automated detection system for pneumonia. Deep learning faces the issue of low-quality X-ray images and biased X-ray image information. The X-ray image is the primary material for creating a transfer learning model. The problem in the dataset led to inaccurate classification results. Many previous works with a deep learning approach have faced inaccurate results. To address the situation mentioned, we propose a novel framework that utilizes two essential mechanisms: advanced image contrast enhancement based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and a hybrid deep learning model combining InceptionResNet and Transformer. Our novel framework is named LUNGINFORMER. The experiment report demonstrated LUNGINFORMER achieved an accuracy of 0.98, a recall of 0.97, an F1-score of 0.98, and a precision of 0.96. According to the AUC test, LUNGINFORMER achieved a tremendous performance with a score of 1.00 for each class. We believed that our performance model was influenced by contrast enhancement and a hybrid deep learning model. |
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| ISSN: | 2442-6571 2548-3161 |