论文标题
胸部X射线图像上的Covid-19严重性分类
COVID-19 Severity Classification on Chest X-ray Images
论文作者
论文摘要
与人工智能（AI）方法结合使用的生物医学成像分析已被证明是非常有价值的，以诊断Covid-19。到目前为止，已经使用了各种分类模型来诊断Covid-19。但是，尚未根据患者的严重程度对患者进行分类。在这项工作中，我们根据感染的严重程度对共证图像进行了分类。首先，我们使用中位过滤器和直方图均衡预处理X射线图像。然后，使用SMOTE技术来增强增强的X射线图像，以实现平衡数据集。然后使用预训练的RESNET50，VGG16模型和SVM分类器进行特征提取和分类。分类模型的结果证实，与替代方案相比，与胸部X射线图像相比，Resnet-50模型在准确性（95％），召回（0.94）和F1得分（0.92）和精度（0.91）方面产生了显着的分类结果（95％）。
Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), and F1-Score (0.92), and precision (0.91).
