Pneumonia is a fatal disease that appears in the lungs and is caused by viral or bacterial infection. Diagnosis of pneumonia in chest X-ray images can be difficult and error-prone because of its similarity with other infections in the lungs. The aim of this study is to develop a computer-aided pneumonia detection system to facilitate the diagnosis decision process. Therefore, a convolutional neural network (CNN) ensemble method was proposed for the automatic diagnosis of pneumonia which is seen in children. In this context, seven well-known CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, and SqueezeNet) pre-trained on the ImageNet dataset were trained with the appropriate transfer learning and fine-tuning strategies on the chest X-ray dataset. Among the seven different models, the three most successful ones were selected for the ensemble method. The final results were obtained by combining the predictions of CNN models with the ensemble method during the test. In addition, a CNN model was trained from scratch, and the results of this model were compared with the proposed ensemble method. The proposed ensemble method achieved remarkable results with an AUC of 95.21 and a sensitivity of 97.76 on the test data. Also, the proposed ensemble method achieved classification accuracy of 90.71 in chest X-ray images as normal, viral pneumonia, and bacterial pneumonia.

中文翻译：

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在胸部 X 射线图像中使用深度卷积神经网络集成诊断小儿肺炎。

肺炎是一种出现在肺部的致命疾病，由病毒或细菌感染引起。在胸部 X 射线图像中诊断肺炎可能很困难且容易出错，因为它与肺部的其他感染相似。本研究的目的是开发一种计算机辅助肺炎检测系统，以促进诊断决策过程。因此，提出了一种卷积神经网络（CNN）集成方法用于儿童肺炎的自动诊断。在这种情况下，在 ImageNet 数据集上预训练的 7 个著名的 CNN 模型（VGG-16、VGG-19、ResNet-50、Inception-V3、Xception、MobileNet 和 SqueezeNet）经过适当的迁移学习和精细训练- 调整胸部 X 射线数据集的策略。在七种不同的型号中，选择了三个最成功的方法用于集成方法。最终结果是在测试过程中将CNN模型的预测与集成方法相结合得到的。此外，从头开始训练了一个 CNN 模型，并将该模型的结果与提出的集成方法进行了比较。所提出的集成方法取得了显着的结果，AUC 为 95.21，对测试数据的敏感性为 97.76。此外，所提出的集成方法在胸部 X 射线图像中实现了 90.71 的分类准确度，如正常、病毒性肺炎和细菌性肺炎。并将该模型的结果与提出的集成方法进行了比较。所提出的集成方法取得了显着的结果，AUC 为 95.21，对测试数据的敏感性为 97.76。此外，所提出的集成方法在胸部 X 射线图像中实现了 90.71 的分类准确度，如正常、病毒性肺炎和细菌性肺炎。并将该模型的结果与提出的集成方法进行了比较。所提出的集成方法取得了显着的结果，AUC 为 95.21，对测试数据的敏感性为 97.76。此外，所提出的集成方法在胸部 X 射线图像中实现了 90.71 的分类准确度，如正常、病毒性肺炎和细菌性肺炎。