| Title |
A new hybrid approach for pneumonia detection using chest X-rays based on ACNN-LSTM and attention mechanism |
| ID_Doc |
61909 |
| Authors |
Lafraxo, S; El Ansari, M; Koutti, L |
| Title |
A new hybrid approach for pneumonia detection using chest X-rays based on ACNN-LSTM and attention mechanism |
| Year |
2024 |
| Published |
|
| DOI |
10.1007/s11042-024-18401-x |
| Abstract |
Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches. |
| Author Keywords |
Pneumonia; Chest X-rays; Adaptive median filter; Convolutional neural network; Long short term memory; Attention mechanism |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001159434300010 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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