Early classification and diagnosis of lung diseases is essential to increase the best chance of patient recovery and survival. Using deep learning to make it possible, the key is how to improve the robustness of the deep learning model and the accuracy of lung image classification. In order to classify the five lung diseases, we used transfer learning to improve and fine-tune the fully connected layer of VGG16, and improve the cross entropy loss function, combined with the gradient boosting decision tree (GBDT), to establish a deep learning model called a classifier. The model was trained using the ChestX-ray14 dataset. On the test set, the classification accuracy of our model for the five lung diseases was 82.43%, 95.37%, 82.11%, 79.81%, 78.13%, which is better than the best published results. The F1 value is 0.456 (95% CI 0.415, 0.496). The robustness of the model exceeds CheXNet and average performance of doctors. This study clarified that the model has strong robustness and effectiveness in classifying five lung diseases.

中文翻译：

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一种肺图像分类方法：结合改进的 VGG16 和梯度提升决策树构建的分类器

肺部疾病的早期分类和诊断对于增加患者康复和生存的最佳机会至关重要。使用深度学习使其成为可能，关键是如何提高深度学习模型的鲁棒性和肺部图像分类的准确性。为了对五种肺部疾病进行分类，我们使用迁移学习对VGG16的全连接层进行改进和微调，并改进交叉熵损失函数，结合梯度提升决策树（GBDT），建立深度学习模型称为分类器。该模型使用 ChestX-ray14 数据集进行训练。在测试集上，我们的模型对五种肺部疾病的分类准确率分别为 82.43%、95.37%、82.11%、79.81%、78.13%，优于已发表的最佳结果。F1 值为 0.456（95% CI 0.415, 0.496）。该模型的鲁棒性超过了 CheXNet 和医生的平均表现。本研究阐明该模型在对五种肺部疾病进行分类时具有很强的稳健性和有效性。