In this work we use the AUCMEDI-Framework to train a deep neural network to classify chest X-ray images as either normal or viral pneumonia. Stratified k-fold cross-validation with k=3 is used to generate the validation-set and 15% of the data are set aside for the evaluation of the models of the different folds and ensembles each. A random-forest ensemble as well as a Soft-Majority-Vote ensemble are built from the predictions of the different folds. Evaluation metrics (Classification-Report, macro f1-scores, Confusion-Matrices, ROC-Curves) of the individual folds and the ensembles show that the classifier works well. Finally Grad-CAM and LIME explainable artificial intelligence (XAI) algorithms are applied to visualize the image features that are most important for the prediction. For Grad-CAM the heatmaps of the three folds are furthermore averaged for all images in order to calculate a mean XAI-heatmap. As the heatmaps of the different folds for most images differ only slightly this averaging procedure works well. However, only medical professionals can evaluate the quality of the features marked by the XAI. A comparison of the evaluation metrics with metrics of standard procedures such as PCR would also be important. Further limitations are discussed.

翻译：在这项工作中,我们使用AUCMEDI-Frameral来训练一个深层神经网络,将胸部X射线图像分类为正常或病毒性肺炎。使用 k=3 的折叠 Kxx 交叉校验,用于生成校验设置,并留出15%的数据用于评估不同折叠和组合的模型。随机的森林组合以及软-Majority-Vote 组合是根据不同折叠的预测构建的。所有图像的评价度量(分类-报告、宏观 F1-岩芯、混凝-矩阵、ROC-C-Curves),用于计算单个折叠叠和集合的模型,显示分类器效果良好。最后,Grad-CAM和LIME可以解释的人工智能(XAI)算法用于对预测最重要的图像特征进行视觉化。对于 Grad-CAM 讨论的三种组合的热度分布图,对于所有图像(分类- Registration-Reports, Commission-Materress, conviews the mass pressal ral ral pressal pressal pressal pressal pressal pressal pressal press) 程序, 只能算算算取出最有细微小的缩缩缩缩缩缩缩缩图。