对比8种神经网络在新冠影像分类中的表现，发现DenseNet和VGG等模型效果最佳。
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection
- 采用迁移学习，用8个预训练模型分析新冠胸部影像
- DenseNet在多分类中准确率达97.64%，VGG等在二分类中精度达99.98%
- 通过热力图可视化模型决策，提升可解释性
本研究对多种神经网络在新冠感染放射影像分类中的表现进行了对比分析。图像来自公开数据集，分为三类：'正常'、'肺炎'和'COVID'。实验采用迁移学习，使用八种预训练网络：SqueezeNet、DenseNet、ResNet、AlexNet、VGG、GoogleNet、ShuffleNet和MobileNet。在多分类任务中，DenseNet结合ADAM优化器达到最高准确率97.64%；在二分类任务中，VGG、ResNet和MobileNet均取得99.98%的最高精确率。同时，通过热力图对模型决策过程进行了可视化对比分析。
原文摘要 · Abstract (English)
This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networks: SqueezeNet, DenseNet, ResNet, AlexNet, VGG, GoogleNet, ShuffleNet, and MobileNet. DenseNet achieved the highest accuracy of 97.64% using the ADAM optimization function in the multiclass approach. In the binary classification approach, the highest precision was 99.98%, obtained by the VGG, ResNet, and MobileNet networks. A comparative evaluation was also conducted using heat maps.
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