融合多源胸片数据，用深度学习提升肺病检测准确率至99%。
Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI
- 整合四大数据集构建混合数据集，增强模型泛化能力。
- VGG16等四模型在混合数据上达99%准确率，稳定识别新冠等疾病。
- 结合LIME解释模型决策，提升医疗AI可解释性，适合临床应用。
先进诊断工具对全球数百万肺病患者至关重要。本研究评估了深度学习与迁移学习模型在混合数据集上的表现，该数据集由孟加拉及全球四个独立数据集合并而成。混合数据集显著提升了模型在检测新冠、肺炎、肺部阴影和正常肺部影像方面的准确率与泛化能力。测试了包括CNN、VGG16、VGG19、InceptionV3、Xception、ResNet50V2、InceptionResNetV2、MobileNetV2和DenseNet121在内的多种模型。结果表明，在混合数据集上，VGG16、Xception、ResNet50V2和DenseNet121均达到99%的准确率，表现出优异且稳定的性能。为揭示模型内部机制，采用可解释AI技术如LIME分析预测过程，尤其在误判案例中提升可解释性，推动可靠、透明的医疗影像人工智能解决方案发展。
原文摘要 · Abstract (English)
Advanced diagnostic instruments are crucial for the accurate detection and treatment of lung diseases, which affect millions of individuals globally. This study examines the effectiveness of deep learning and transfer learning models using a hybrid dataset, created by merging four individual datasets from Bangladesh and global sources. The hybrid dataset significantly enhances model accuracy and generalizability, particularly in detecting COVID-19, pneumonia, lung opacity, and normal lung conditions from chest X-ray images. A range of models, including CNN, VGG16, VGG19, InceptionV3, Xception, ResNet50V2, InceptionResNetV2, MobileNetV2, and DenseNet121, were applied to both individual and hybrid datasets. The results showed superior performance on the hybrid dataset, with VGG16, Xception, ResNet50V2, and DenseNet121 each achieving an accuracy of 99%. This consistent performance across the hybrid dataset highlights the robustness of these models in handling diverse data while maintaining high accuracy. To understand the models implicit behavior, explainable AI techniques were employed to illuminate their black-box nature. Specifically, LIME was used to enhance the interpretability of model predictions, especially in cases of misclassification, contributing to the development of reliable and interpretable AI-driven solutions for medical imaging.
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。