  Chest radiographs are among the most frequently acquired images in radiology
and are often the subject of computer vision research. However, most of the
models used to classify chest radiographs are derived from openly available
deep neural networks, trained on large image-datasets. These datasets routinely
differ from chest radiographs in that they are mostly color images and contain
several possible image classes, while radiographs are greyscale images and
often only contain fewer image classes. Therefore, very deep neural networks,
which can represent more complex relationships in image-features, might not be
required for the comparatively simpler task of classifying grayscale chest
radiographs. We compared fifteen different architectures of artificial neural
networks regarding training-time and performance on the openly available
CheXpert dataset to identify the most suitable models for deep learning tasks
on chest radiographs. We could show, that smaller networks such as ResNet-34,
AlexNet or VGG-16 have the potential to classify chest radiographs as precisely
as deeper neural networks such as DenseNet-201 or ResNet-151, while being less
computationally demanding.
