用深度学习区分肺部X光片中的四种疾病，准确率高且可解释。
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method
- 基于预训练CNN模型迁移学习，微调医疗X光图像数据
- 四类疾病分类准确率高，精确率、召回率、F1值均表现优异
- 结合Grad-CAM可视化分析，提升临床应用可信度
本文研究多种分类模型在将胸部X光片分为新冠、肺炎、结核（TB）和正常四类中的表现。采用先进的预训练卷积神经网络（CNN）模型，通过迁移学习在标注的医学X光图像上进行微调。初步结果令人鼓舞，准确率高，在精确率、召回率和F1分数等关键指标上表现强劲。引入梯度加权类激活映射（Grad-CAM）提升模型可解释性，为分类决策提供视觉依据，增强临床应用中的信任与透明度。
原文摘要 · Abstract (English)
In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. The initial results are promising with high accuracy and strong performance in key classification metrics such as precision, recall, and F1 score. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability to provide visual explanations for classification decisions, improving trust and transparency in clinical applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。