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

A novel method to enhance pneumonia detection via a model-level  ensembling of CNN and vision transformer

As the COVID-19 pandemic continues to devastate globally, the use of chest
X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing
continues to grow given its routine clinical use for respiratory complaint. As
part of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an
enhanced deep convolutional neural network design for COVID-19 detection from
CXR images built using a greater quantity and diversity of patients than the
original COVID-Net. To facilitate this, we also introduce a new benchmark
dataset composed of 19,203 CXR images from a multinational cohort of 16,656
patients from at least 51 countries, making it the largest, most diverse
COVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves
sensitivity and positive predictive value of 95.5%/97.0%, respectively, and was
audited in a transparent and responsible manner. Explainability-driven
performance validation was used during auditing to gain deeper insights in its
decision-making behaviour and to ensure clinically relevant factors are
leveraged for improving trust in its usage. Radiologist validation was also
conducted, where select cases were reviewed and reported on by two
board-certified radiologists with over 10 and 19 years of experience,
respectively, and showed that the critical factors leveraged by COVID-Net CXR-2
are consistent with radiologist interpretations. While not a production-ready
solution, we hope the open-source, open-access release of COVID-Net CXR-2 and
the respective CXR benchmark dataset will encourage researchers, clinical
scientists, and citizen scientists to accelerate advancements and innovations
in the fight against the pandemic.

COVID-Net CXR-2 是一种新型深度卷积神经网络，可用于从 CXR 图像中检测 COVID-19。该研究建立了拥有多个国家的 16656 名患者的 19203 幅 CXR 图像的基准数据集，成为最大且最多样化的开放获取的 COVID-19 CXR 数据集。该网络可实现 95.5% 的灵敏度和 97.0% 的阳性预测值，并得到透明和负责任的审核。