Pneumonia remains a leading cause of morbidity and mortality worldwide. Chest X-ray (CXR) imaging is a fundamental diagnostic tool, but traditional analysis relies on time-intensive expert evaluation. Recently, deep learning has shown immense potential for automating pneumonia detection from CXRs. This paper explores applying neural networks to improve CXR-based pneumonia diagnosis. We developed a novel model fusing Convolution Neural networks (CNN) and Vision Transformer networks via model-level ensembling. Our fusion architecture combines a ResNet34 variant and a Multi-Axis Vision Transformer small model. Both base models are initialized with ImageNet pre-trained weights. The output layers are removed, and features are combined using a flattening layer before final classification. Experiments used the Kaggle pediatric pneumonia dataset containing 1,341 normal and 3,875 pneumonia CXR images. We compared our model against standalone ResNet34, Vision Transformer, and Swin Transformer Tiny baseline models using identical training procedures. Extensive data augmentation, Adam optimization, learning rate warmup, and decay were employed. The fusion model achieved a state-of-the-art accuracy of 94.87%, surpassing the baselines. We also attained excellent sensitivity, specificity, kappa score, and positive predictive value. Confusion matrix analysis confirms fewer misclassifications. The ResNet34 and Vision Transformer combination enables jointly learning robust features from CNNs and Transformer paradigms. This model-level ensemble technique effectively integrates their complementary strengths for enhanced pneumonia classification.

通过模型融合的方式，将卷积神经网络和视觉Transformer网络结合起来，以提高基于胸部 X 射线图像的肺炎诊断的准确性。融合模型在Kaggle小儿肺炎数据集上取得了94.87%的准确率，比基准模型表现更好。

通过卷积神经网络和视觉变换器的模型级集成来提高肺炎检测的新方法