用胸部X光片训练模型，自动识别患者疾病，效果优于传统方法。
Machine learning and machine learned prediction in chest X-ray images
- 对比基础CNN与DenseNet-121模型，提升诊断准确率
- 在5824张胸片上实现高精度二分类，准确率显著
- 可视化显示DenseNet-121更聚焦病灶区域，决策更可信
机器学习与人工智能通过数据训练算法、发现模式并做出预测，在无需显式编程的情况下解决复杂问题。本研究基于5824张胸部X光片，采用基础卷积神经网络（CNN）和DenseNet-121两种机器学习算法，对患者患病情况进行预测分析。实验表明，两种模型在本研究的二分类任务中表现优异。梯度加权类激活映射（Grad-CAM）显示，DenseNet-121在决策过程中更准确地关注输入图像中的关键部位，优于基础CNN。
原文摘要 · Abstract (English)
Machine learning and artificial intelligence are fast-growing fields of research in which data is used to train algorithms, learn patterns, and make predictions. This approach helps to solve seemingly intricate problems with significant accuracy without explicit programming by recognizing complex relationships in data. Taking an example of 5824 chest X-ray images, we implement two machine learning algorithms, namely, a baseline convolutional neural network (CNN) and a DenseNet-121, and present our analysis in making machine-learned predictions in predicting patients with ailments. Both baseline CNN and DenseNet-121 perform very well in the binary classification problem presented in this work. Gradient-weighted class activation mapping shows that DenseNet-121 correctly focuses on essential parts of the input chest X-ray images in its decision-making more than the baseline CNN.
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