用深度学习从X光片早筛肺病，提升诊断速度与准确性。
Leveraging Machine Learning for Early Detection of Lung Diseases
- 结合传统图像处理与多种神经网络模型
- 在新冠、肺癌、肺炎诊断中达高准确率
- 适合医疗资源匮乏地区使用
将传统图像处理方法与先进神经网络相结合，构建预测性与预防性医疗范式。本研究提供快速、精准且非侵入式的诊断方案，显著改善患者预后，尤其适用于缺乏放射科医生和医疗资源的地区。项目中应用深度学习技术，基于胸部X光片对新冠、肺癌和肺炎等呼吸系统疾病进行诊断。我们训练并验证了多种神经网络模型，包括CNN、VGG16、InceptionV3和EfficientNetB0，各项指标表现优异，准确率、精确率、召回率和F1分数均达到高水平，凸显其在实际诊断中的可靠性和应用潜力。
原文摘要 · Abstract (English)
A combination of traditional image processing methods with advanced neural networks concretes a predictive and preventive healthcare paradigm. This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly impact patient outcomes, particularly in areas with limited access to radiologists and healthcare resources. In this project, deep learning methods apply in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays. We trained and validated various neural network models, including CNNs, VGG16, InceptionV3, and EfficientNetB0, with high accuracy, precision, recall, and F1 scores to highlight the models' reliability and potential in real-world diagnostic applications.
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