Establishment and application of a deep learning-based model for pneumonia detection in children(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第12期
- Page:
- 1579-1584
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Establishment and application of a deep learning-based model for pneumonia detection in children
- Author(s):
- DONG Fangfen1; 2; CHEN Qun3; LI Nuoxi2; XU Benhua1; 2; LI Xiaobo1; 2; 4
- 1. Department of Radiation Oncology, Fujian Medical University Union Hospital/Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors/Clinical Research Center for Radiology and Radiotherapy for Digestive, Hematological and Breast Malignancies of Fujian Province, Fuzhou 350001, China 2. School of Medical Imaging, Fujian Medical University, Fuzhou 350004, China 3. School of Computer Science, Northwestern Polytechnical University, Xian 710072, China 4. Department of Engineering Physics, Tsinghua University, Beijing 100084, China
- Keywords:
- Keywords: children pneumonia deep learning neural network
- PACS:
- R318；R445.4
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.12.020
- Abstract:
- Abstract: Objective To construct a deep learning-based model for automatically detecting pneumonia according to the digital ortho-images of childrens chest X-ray for assisting clinical diagnosis and improving the efficiency of image diagnosis. Methods A total of 5 856 pediatric chest radiographs, including 4 273 chest radiographs of pneumonia and 1 583 normal chest radiographs, were selected from the public data set and divided into training set, verification set and test set. A model for the automated pediatric pneumonia detection was constructed based on Resnet-50. The validation set was used for selecting the optimal model, and the test set for carrying out internal independent validation. In addition, 611 pediatric chest radiographs, including 300 chest radiographs of pneumonia and 311 normal chest radiographs, were further collected from 6 medical units for external validation, and the model was fine-tuned according to validation results and then tested again to make it more suitable for clinical application. Results An automated detection model for pediatric pneumonia was successfully constructed using deep learning technology and public data set. The accuracy, precision, recall, F1-score and AUC of the model were 98.48%, 99.54%, 98.81%, 98.86% and 0.999, respectively. After fine-tuning the model with some external validation data, the accuracy of the independent test was improved from 59.90% (preliminary external validation) to 85.00% (independent test). Conclusion It is feasible to construct an automated pneumonia detection model using deep learning and public data set, and the accuracy of the model can reach 98.48%. In practice, the model should be fine-tuned by selecting the appropriate data set according to the specific conditions.
Last Update: 2022-12-23