Pneumonia is a common infection that inflames the air sacs in the lungs, causing symptoms such as difficulty breathing and fever. Although pneumonia is not difficult to treat, prompt diagnosis is crucial. Without proper treatment, pneumonia can be fatal, especially in children and the elderly. Chest x-rays are an affordable way to diagnose pneumonia. Investigating an algorithmic model that can reliably and intelligently classify pneumonia based on chest X-ray images could greatly reduce the burden on physicians. The advantages and disadvantages of each of the four convolutional neural networks VGG16, ResNet50, DenseNet201, and DWA algorithm models are analyzed and given by comparing and investigating each model. The VGG16, ResNet50, and DenseNet201 network models are compared with the DWA model. When training the depthwise separable convolution with attention neural network (DWA), the training accuracy reaches 97.5%. The validation accuracy was 79% due to the model’s tendency to overfit, and the test dataset had 1175 X-ray images with a test accuracy of 96.1%. The experimental results illustrate the effectiveness of the attention mechanism and the reliability of the deeply separable convolutional neural network algorithm. The successful application of the deep learning algorithm proposed in this paper on pneumonia recognition will provide an objective, accurate, and fast solution for medical practitioners and can provide a fast and accurate pneumonia diagnosis system for doctors.

中文翻译：

---

基于深度卷积和注意力神经网络的肺炎智能识别算法研究

肺炎是一种常见的感染，会使肺部的气囊发炎，引起呼吸困难和发烧等症状。尽管肺炎不难治疗，但及时诊断至关重要。如果没有适当的治疗，肺炎可能是致命的，尤其是在儿童和老年人中。胸部 X 光检查是一种经济实惠的诊断肺炎的方法。研究一种能够根据胸部 X 射线图像对肺炎进行可靠、智能分类的算法模型，可以大大减轻医生的负担。通过对比研究各个模型，分析给出了四种卷积神经网络VGG16、ResNet50、DenseNet201、DWA算法模型各自的优缺点。VGG16、ResNet50 和 DenseNet201 网络模型与 DWA 模型进行了比较。使用注意力神经网络（DWA）训练深度可分离卷积时，训练准确率达到97.5%。由于模型有过拟合倾向，验证准确率为 79%，测试数据集有 1175 张 X 射线图像，测试准确率为 96.1%。实验结果说明了注意力机制的有效性和深度可分离卷积神经网络算法的可靠性。本文提出的深度学习算法在肺炎识别上的成功应用，将为医生提供客观、准确、快速的解决方案，为医生提供快速准确的肺炎诊断系统。测试数据集有1175张X射线图像，测试准确率为96.1%。实验结果说明了注意力机制的有效性和深度可分离卷积神经网络算法的可靠性。本文提出的深度学习算法在肺炎识别上的成功应用，将为医生提供客观、准确、快速的解决方案，为医生提供快速准确的肺炎诊断系统。测试数据集有1175张X射线图像，测试准确率为96.1%。实验结果说明了注意力机制的有效性和深度可分离卷积神经网络算法的可靠性。本文提出的深度学习算法在肺炎识别上的成功应用，将为医生提供客观、准确、快速的解决方案，为医生提供快速准确的肺炎诊断系统。