  Automated medical image classification with convolutional neural networks
(CNNs) has great potential to impact healthcare, particularly in
resource-constrained healthcare systems where fewer trained radiologists are
available. However, little is known about how well a trained CNN can perform on
images with the increased noise levels, different acquisition protocols, or
additional artifacts that may arise when using low-cost scanners, which can be
underrepresented in datasets collected from well-funded hospitals. In this
work, we investigate how a model trained to triage head computed tomography
(CT) scans performs on images acquired with reduced x-ray tube current, fewer
projections per gantry rotation, and limited angle scans. These changes can
reduce the cost of the scanner and demands on electrical power but come at the
expense of increased image noise and artifacts. We first develop a model to
triage head CTs and report an area under the receiver operating characteristic
curve (AUROC) of 0.77. We then show that the trained model is robust to reduced
tube current and fewer projections, with the AUROC dropping only 0.65% for
images acquired with a 16x reduction in tube current and 0.22% for images
acquired with 8x fewer projections. Finally, for significantly degraded images
acquired by a limited angle scan, we show that a model trained specifically to
classify such images can overcome the technological limitations to
reconstruction and maintain an AUROC within 0.09% of the original model.
