  Chest radiographs are used for the diagnosis of multiple critical illnesses
(e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the
automatic or semi-automatic analysis of these data are of particular interest.
An efficient analysis of large amounts of chest radiographs can aid physicians
and radiologists, ultimately allowing for better medical care of lung-, heart-
and chest-related conditions. We propose a novel Discrete Wavelet Transform
(DWT)-based method for the efficient identification and encoding of visual
information that is typically lost in the down-sampling of high-resolution
radiographs, a common step in computer-aided diagnostic pipelines. Our proposed
approach requires only slight modifications to the input of existing
state-of-the-art Convolutional Neural Networks (CNNs), making it easily
applicable to existing image classification frameworks. We show that the extra
high-frequency components offered by our method increased the classification
performance of several CNNs in benchmarks employing the NIH Chest-8 and
ImageNet-2017 datasets. Based on our results we hypothesize that providing
frequency-specific coefficients allows the CNNs to specialize in the
identification of structures that are particular to a frequency band,
ultimately increasing classification performance, without an increase in
computational load. The implementation of our work is available at
github.com/DeclanMcIntosh/LeGallCuda.
