用X光片识别肺炎类型，准确率超91%
Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks
- 用预训练CNN+特征筛选+SVM分类
- 无肺炎与肺炎区分准确率91.02%
- 可区分病毒与细菌性肺炎，适合临床辅助诊断
世界卫生组织（WHO）指出，肺炎每年导致大量死亡。为此，本文提出一种决策支持系统，通过在胸部X光片（CXR）上应用迁移学习（TL）的卷积神经网络（CNN）模型，对患者进行分类：无肺炎、病毒性肺炎或细菌性肺炎。系统结合了Relief和卡方检验作为降维方法，并采用支持向量机（SVM）进行分类。实验结果表明，在区分无肺炎与肺炎患者时，准确率达91.02%，精确率97.73%，召回率98.03%，F1分数97.88%；在区分病毒性肺炎与细菌性肺炎时，准确率为93.66%，精确率94.26%，召回率92.66%，F1分数93.45%。
原文摘要 · Abstract (English)
According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this issue, the development of a decision support system for the classification of patients into those without pneumonia and those with viral or bacterial pneumonia is proposed. This is achieved by implementing transfer learning (TL) using pre-trained convolutional neural network (CNN) models on chest x-ray (CXR) images. The system is further enhanced by integrating Relief and Chi-square methods as dimensionality reduction techniques, along with support vector machines (SVM) for classification. The performance of a series of experiments was evaluated to build a model capable of distinguishing between patients without pneumonia and those with viral or bacterial pneumonia. The obtained results include an accuracy of 91.02%, precision of 97.73%, recall of 98.03%, and an F1 Score of 97.88% for discriminating between patients without pneumonia and those with pneumonia. In addition, accuracy of 93.66%, precision of 94.26%, recall of 92.66%, and an F1 Score of 93.45% were achieved for discriminating between patients with viral pneumonia and those with bacterial pneumonia.
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