发表机构
University of South Dakota（南达科他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较多种CNN模型检测胸部X光肺炎，MobileNetV2准确率最高（88%），GAN增强未提升验证性能，需进一步优化。
AI 中文摘要
本研究使用VGG19、MobileNetV2、ResNet50和自定义CNN评估胸部X光中的肺炎分类，并探索基于生成对抗网络（GAN）的合成数据增强。MobileNetV2取得了报告的最高准确率88%，且类别间性能均衡。自定义CNN实现了92.67%的肺炎召回率和79.43%的精确率，凸显了精确率-召回率之间的权衡。研究采用准确率、F1分数、精确率、召回率、混淆矩阵和训练曲线来评估性能。将合成肺炎图像与真实图像结合，以探究数据增强能否提升分类性能。在报告的VGG19对比中，增强数据的训练准确率接近100%，而验证准确率仍接近50%，低于真实数据的验证准确率。因此，该实验未证明GAN增强能带来验证性能的提升。分类器对比凸显了准确率和肺炎召回率的差异，而增强实验表明需进一步评估合成图像质量和训练设置。
英文摘要
This study evaluates pneumonia classification in chest X-rays using VGG19, MobileNetV2, ResNet50, and a custom CNN, and explores Generative Adversarial Network (GAN)-based synthetic data augmentation. MobileNetV2 achieved the highest reported accuracy of 88% with balanced class-wise performance. The custom CNN achieved pneumonia recall of 92.67% and precision of 79.43%, highlighting a precision-recall trade-off. Accuracy, F1-score, precision, recall, confusion matrices, and training curves were used to assess performance. Synthetic pneumonia images were combined with real images to investigate whether augmentation could improve classification performance. In the reported VGG19 comparison, augmented-data training accuracy reached approximately 100%, while validation accuracy remained near 50%, below the real-data validation accuracy. This experiment therefore did not demonstrate a validation-performance benefit from GAN augmentation. The classifier comparison highlights differences in accuracy and pneumonia recall, while the augmentation experiment indicates the need for further evaluation of synthetic-image quality and training settings.
