Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14" dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays.

通过深度学习技术，我们使用多种预训练的卷积神经网络 (CNN)、Transformer、混合 (CNN+Transformer) 模型和经典模型，在“ChestX-ray14”数据集上进行实验。最佳的单一模型是CoAtNet，其在接收器工作特性曲线下的面积（AUROC）为84.2%。通过将所有经过训练的模型的预测组合，使用加权平均集成方法，其中每个模型的权重是通过差分进化确定的，我们进一步提高了AUROC至85.4%，在该领域超越了其他最先进的方法。我们的研究结果表明，深度学习技术尤其是集成深度学习，有潜力提高从胸部X射线图像中自动诊断胸部疾病的准确性。

SynthEnsemble：基于CNN、视觉Transformer和混合模型的多标签胸部X光分类的融合