Attention-enhanced architecture for improved pneumonia detection in chest X-ray images
Abstract In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mech...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-023-01177-1 |
| _version_ | 1797362776751996928 |
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| author | Dikai Li |
| author_facet | Dikai Li |
| author_sort | Dikai Li |
| collection | DOAJ |
| description | Abstract In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model’s performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model’s spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning. |
| first_indexed | 2024-03-08T16:11:24Z |
| format | Article |
| id | doaj.art-684bc74b979e4b328b626bf60cbeb078 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-08T16:11:24Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-684bc74b979e4b328b626bf60cbeb0782024-01-07T12:54:24ZengBMCBMC Medical Imaging1471-23422024-01-0124111310.1186/s12880-023-01177-1Attention-enhanced architecture for improved pneumonia detection in chest X-ray imagesDikai Li0Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology UniversityAbstract In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model’s performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model’s spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning.https://doi.org/10.1186/s12880-023-01177-1Pneumonia detectionAttention-enhanced architectureImbalanced training samplesMedical imaging |
| spellingShingle | Dikai Li Attention-enhanced architecture for improved pneumonia detection in chest X-ray images BMC Medical Imaging Pneumonia detection Attention-enhanced architecture Imbalanced training samples Medical imaging |
| title | Attention-enhanced architecture for improved pneumonia detection in chest X-ray images |
| title_full | Attention-enhanced architecture for improved pneumonia detection in chest X-ray images |
| title_fullStr | Attention-enhanced architecture for improved pneumonia detection in chest X-ray images |
| title_full_unstemmed | Attention-enhanced architecture for improved pneumonia detection in chest X-ray images |
| title_short | Attention-enhanced architecture for improved pneumonia detection in chest X-ray images |
| title_sort | attention enhanced architecture for improved pneumonia detection in chest x ray images |
| topic | Pneumonia detection Attention-enhanced architecture Imbalanced training samples Medical imaging |
| url | https://doi.org/10.1186/s12880-023-01177-1 |
| work_keys_str_mv | AT dikaili attentionenhancedarchitectureforimprovedpneumoniadetectioninchestxrayimages |