Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays
Abstract Background Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep lear...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Pediatrics |
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| Online Access: | https://doi.org/10.1186/s12887-024-05204-0 |
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| author | Xing-hao Lan Yun-xu Zhang Wei-hua Yuan Fei Shi Wan-liang Guo |
| author_facet | Xing-hao Lan Yun-xu Zhang Wei-hua Yuan Fei Shi Wan-liang Guo |
| author_sort | Xing-hao Lan |
| collection | DOAJ |
| description | Abstract Background Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia. Methods We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets. Three deep convolutional neural networks (ResNet50, DenseNet121, and EfficientNetv2-S) were used to distinguish mycoplasma pneumonia from viral pneumonia based on CXR. Accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of the model. Visualization was also achieved through the use of Class Activation Mapping (CAM), providing more transparent and interpretable classification results. Results Of the three models evaluated, ResNet50 outperformed the others. Pretrained with the ZhangLabData dataset, the ResNet50 model achieved 80.00% accuracy in the validation set. The model also showed robustness in two test sets, with accuracy of 82.65 and 83.27%, and AUC values of 0.822 and 0.758. In the test results using ImageNet pre-training weights, the accuracy of the ResNet50 model in the validation set was 80.00%; the accuracy in the two test sets was 81.63 and 62.91%; and the corresponding AUC values were 0.851 and 0.776. The sensitivity values were 0.884 and 0.595, and the specificity values were 0.655 and 0.814. Conclusions This study demonstrates that deep convolutional networks utilizing transfer learning are effective in detecting mycoplasma pneumonia based on chest X-rays (CXR). This suggests that, in the near future, such computer-aided detection approaches can be employed for the early screening of pneumonia pathogens. This has significant clinical implications for the rapid diagnosis and appropriate medical intervention of pneumonia, potentially enhancing the prognosis for affected children. |
| format | Article |
| id | doaj-art-295851b9f6e84cadb543e6d1cfcfc4d2 |
| institution | Kabale University |
| issn | 1471-2431 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pediatrics |
| spelling | doaj-art-295851b9f6e84cadb543e6d1cfcfc4d22024-11-17T12:48:59ZengBMCBMC Pediatrics1471-24312024-11-0124111110.1186/s12887-024-05204-0Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-raysXing-hao Lan0Yun-xu Zhang1Wei-hua Yuan2Fei Shi3Wan-liang Guo4Radiology department, Children’s Hospital of Soochow UniversitySchool of Electronic and Information Engineering, Soochow UniversityRadiology department, Changzhou Children’s Hospital of Nantong UniversitySchool of Electronic and Information Engineering, Soochow UniversityRadiology department, Children’s Hospital of Soochow UniversityAbstract Background Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia. Methods We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets. Three deep convolutional neural networks (ResNet50, DenseNet121, and EfficientNetv2-S) were used to distinguish mycoplasma pneumonia from viral pneumonia based on CXR. Accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of the model. Visualization was also achieved through the use of Class Activation Mapping (CAM), providing more transparent and interpretable classification results. Results Of the three models evaluated, ResNet50 outperformed the others. Pretrained with the ZhangLabData dataset, the ResNet50 model achieved 80.00% accuracy in the validation set. The model also showed robustness in two test sets, with accuracy of 82.65 and 83.27%, and AUC values of 0.822 and 0.758. In the test results using ImageNet pre-training weights, the accuracy of the ResNet50 model in the validation set was 80.00%; the accuracy in the two test sets was 81.63 and 62.91%; and the corresponding AUC values were 0.851 and 0.776. The sensitivity values were 0.884 and 0.595, and the specificity values were 0.655 and 0.814. Conclusions This study demonstrates that deep convolutional networks utilizing transfer learning are effective in detecting mycoplasma pneumonia based on chest X-rays (CXR). This suggests that, in the near future, such computer-aided detection approaches can be employed for the early screening of pneumonia pathogens. This has significant clinical implications for the rapid diagnosis and appropriate medical intervention of pneumonia, potentially enhancing the prognosis for affected children.https://doi.org/10.1186/s12887-024-05204-0Mycoplasma pneumoniaePneumoniaDeep learningX-rayPediatric |
| spellingShingle | Xing-hao Lan Yun-xu Zhang Wei-hua Yuan Fei Shi Wan-liang Guo Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays BMC Pediatrics Mycoplasma pneumoniae Pneumonia Deep learning X-ray Pediatric |
| title | Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays |
| title_full | Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays |
| title_fullStr | Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays |
| title_full_unstemmed | Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays |
| title_short | Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays |
| title_sort | image based deep learning in diagnosing mycoplasma pneumonia on pediatric chest x rays |
| topic | Mycoplasma pneumoniae Pneumonia Deep learning X-ray Pediatric |
| url | https://doi.org/10.1186/s12887-024-05204-0 |
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