标题： 视觉变压器与传统深度学习方法在胸部X光片自动肺炎检测中的比较分析 显示英文标题

标题： Comparative Analysis of Vision Transformers and Traditional Deep Learning Approaches for Automated Pneumonia Detection in Chest X-Rays

摘要： 肺炎，特别是由像COVID-19这样的疾病引起的肺炎，仍然是一个需要快速和准确诊断的关键全球健康挑战。 本研究全面比较了传统机器学习和最先进的深度学习方法，用于使用胸部X光片（CXRs）的自动肺炎检测。 我们评估了多种方法，从传统的机器学习技术（基于PCA的聚类、逻辑回归和支持向量分类）到先进的深度学习架构，包括卷积神经网络（修改后的LeNet、DenseNet-121）和各种视觉变压器（ViT）实现（Deep-ViT、紧凑型卷积变压器和Cross-ViT）。 使用包含5,856张儿童胸部X光图像的数据集，我们证明视觉变压器，尤其是Cross-ViT架构，在准确率88.25%和召回率99.42%方面表现出色，超过了传统的CNN方法。 我们的分析表明，架构选择对性能的影响比模型大小更为显著，Cross-ViT的75M参数优于更大的模型。 该研究还考虑了实际因素，包括计算效率、训练需求以及在医学诊断中精确度和召回率之间的关键平衡。 我们的研究结果表明，视觉变压器为自动肺炎检测提供了一个有前景的方向，可能在健康危机期间实现更快和更准确的诊断。 显示英文摘要

摘要： Pneumonia, particularly when induced by diseases like COVID-19, remains a critical global health challenge requiring rapid and accurate diagnosis. This study presents a comprehensive comparison of traditional machine learning and state-of-the-art deep learning approaches for automated pneumonia detection using chest X-rays (CXRs). We evaluate multiple methodologies, ranging from conventional machine learning techniques (PCA-based clustering, Logistic Regression, and Support Vector Classification) to advanced deep learning architectures including Convolutional Neural Networks (Modified LeNet, DenseNet-121) and various Vision Transformer (ViT) implementations (Deep-ViT, Compact Convolutional Transformer, and Cross-ViT). Using a dataset of 5,856 pediatric CXR images, we demonstrate that Vision Transformers, particularly the Cross-ViT architecture, achieve superior performance with 88.25% accuracy and 99.42% recall, surpassing traditional CNN approaches. Our analysis reveals that architectural choices impact performance more significantly than model size, with Cross-ViT's 75M parameters outperforming larger models. The study also addresses practical considerations including computational efficiency, training requirements, and the critical balance between precision and recall in medical diagnostics. Our findings suggest that Vision Transformers offer a promising direction for automated pneumonia detection, potentially enabling more rapid and accurate diagnosis during health crises.