Arkan, EmreBeckert, Jan MalteGesellschaft für Informatik2021-12-152021-12-152021978-3-88579-751-7https://dl.gi.de/handle/20.500.12116/37773This work demonstrates how Convolutional Neural Networks ( CNN s) can be used to identify signs of COVID-19 from Chest X-rays (CXR s) and discusses the challenges of deep learning with small datasets. In order to validate the model’s performance, two novel explanation methods LIME and Grad-CAM are explored. Additionally, they serve to further increase users’ confidence in specific classifications. Since the explanation results revealed model biases, additional preprocessing mechanisms were explored: A U-Net-based lung segmenter is introduced to the preprocessing pipeline, which masks all non-lung parts of the CXRs images. Subsequently, the segmentation and non-segmentation results were evaluated with regard to both their performance metrics and interpreted explanation results.enCOVID-19Chest X-rayCNNGrad-CAMLIMEExplainable AI1614-3213