Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study

作者：Kairou Guo, Jiangbo Cheng, Kaiyuan Li, Lanhui Wang, Yadong Lv, Desen Cao · 发表于：BMC Medical Imaging · 年份：2023 · DOI：10.1186/s12880-023-01174-4 · 被引用次数：20 · 研究领域：COVID-19 diagnosis using AI、Domain Adaptation and Few-Shot Learning、AI in cancer detection

PURPOSE: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray. METHODS: This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions. RESULTS: The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area. CONCLUSION: The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area ...