Wang Cheng, Xu Chang, Zhang Yulai, Lu Peng
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Institute of Computer Innovation Technology, Zhejiang University, Hangzhou 310023, China.
Diagnostics (Basel). 2023 Jun 20;13(12):2125. doi: 10.3390/diagnostics13122125.
Pneumonia is an acute respiratory infection that affects the lungs. It is the single largest infectious disease that kills children worldwide. According to a 2019 World Health Organization survey, pneumonia caused 740,180 deaths in children under 5 years of age, accounting for 14% of all deaths in children under 5 years of age but 22% of all deaths in children aged 1 to 5 years. This shows that early recognition of pneumonia in children is particularly important. In this study, we propose a pneumonia binary classification model for chest X-ray image recognition based on a deep learning approach. We extract features using a traditional convolutional network framework to obtain features containing rich semantic information. The adjacency matrix is also constructed to represent the degree of relevance of each region in the image. In the final part of the model, we use graph inference to complete the global modeling to help classify pneumonia disease. A total of 6189 children's X-ray films containing 3319 normal cases and 2870 pneumonia cases were used in the experiment. In total, 20% was selected as the test data set, and 11 common models were compared using 4 evaluation metrics, of which the accuracy rate reached 89.1% and the F1-score reached 90%, achieving the optimum.
肺炎是一种影响肺部的急性呼吸道感染。它是全球范围内导致儿童死亡的单一最大传染病。根据世界卫生组织2019年的一项调查，肺炎导致5岁以下儿童死亡740180例，占5岁以下儿童全部死亡人数的14%，但占1至5岁儿童全部死亡人数的22%。这表明早期识别儿童肺炎尤为重要。在本研究中，我们基于深度学习方法提出了一种用于胸部X光图像识别的肺炎二元分类模型。我们使用传统卷积网络框架提取特征，以获得包含丰富语义信息的特征。还构建邻接矩阵来表示图像中每个区域的相关程度。在模型的最后部分，我们使用图推理完成全局建模，以帮助对肺炎疾病进行分类。实验共使用了6189张儿童X光片，其中正常病例3319例，肺炎病例2870例。总共选取20%作为测试数据集，并使用4种评估指标对11种常见模型进行比较，其中准确率达到89.1%，F1分数达到90%，达到了最优效果。
