标题： 使用深度学习分类方法的胸病检测在 X 光图像中的应用 显示英文标题

标题： Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method

摘要： 在这项工作中，我们研究了多种分类模型在将胸透X光片分类为COVID-19、肺炎、结核病（TB）和正常病例四种类别的性能。 我们利用了最先进的预训练卷积神经网络（CNNs）的迁移学习技术。 我们在标记的医学X光片上微调了这些预训练架构。 初步结果显示，在精度、召回率和F1分数等关键分类指标方面具有高准确率和强大性能。 我们应用了梯度加权类激活映射（Grad-CAM）以提高模型的可解释性，为分类决策提供可视化解释，从而提高临床应用中的信任和透明度。 显示英文摘要

摘要： 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.