标题： 冠状病毒检测中X射线图像分类的神经网络模型比较 显示英文标题

标题： Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection

摘要： 本研究提出了对检测放射影像中冠状病毒肺炎感染方法的比较分析。 这些图像来源于公开数据集，被分为三类：'正常'、'肺炎'和 '冠状病毒'。 在实验中，使用八种预训练网络进行了迁移学习：SqueezeNet、DenseNet、ResNet、AlexNet、VGG、GoogleNet、 ShuffleNet 和 MobileNet。 在多类方法中，DenseNet 使用 ADAM 优化函数达到了最高的准确率 97.64%。 在二分类方法中，最高的精确度为 99.98%，由 VGG、 ResNet 和 MobileNet 网络获得。 还使用热图进行了比较评估。 显示英文摘要

摘要： 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.