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Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images
1Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
Abstract:
Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.
Insights
A new multi-branch fusion auxiliary learning (MBFAL) method rapidly and accurately screens lung infections from chest X-ray (CXR) images. This AI approach achieves high accuracy in detecting normal cases, COVID-19, and other viral or bacterial pneumonia.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Diseases
Background:
- Timely screening and isolation are crucial for bacterial and viral lung infections.
- Different pneumonia types necessitate distinct treatment strategies.
- Accurate pneumonia detection from chest X-ray (CXR) images is critical for effective patient management.
Purpose of the Study:
- To develop a rapid and accurate screening method for lung infections using CXR images.
- To introduce the multi-branch fusion auxiliary learning (MBFAL) method for enhanced pneumonia detection.
- To improve the classification accuracy of various pneumonia types, including COVID-19.
Main Methods:
- Proposed a multi-branch fusion auxiliary learning (MBFAL) method utilizing a double-branch neural network.
- Implemented a dual-task approach: classifying normal, COVID-19, other viral, and bacterial pneumonia, and classifying the three pneumonia types.
- Employed auxiliary learning where the second task assists the first, fusing feature maps after sample screening for improved pneumonia case recognition.
Main Results:
- Achieved an average classification accuracy of 95.61% for pneumonia detection.
- Reported high single-class accuracies: normal (98.70%), COVID-19 (99.10%), other viral pneumonia (96.60%), and bacterial pneumonia (96.80%).
- Demonstrated superior performance compared to baseline models and individually constructed methods for rapid pneumonia screening.
Conclusions:
- The MBFAL method offers a significant advancement in the rapid and accurate screening of lung infections from CXR images.
- The auxiliary learning strategy effectively enhances the model's ability to identify pneumonia cases without compromising normal case recognition.
- MBFAL shows promise for clinical application in improving diagnostic efficiency and patient outcomes for pneumonia.
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