  Coccidioidomycosis is the most common systemic mycosis in dogs in the
southwestern United States. With warming climates, affected areas and number of
cases are expected to increase in the coming years, escalating also the chances
of transmission to humans. As a result, developing methods for automating the
detection of the disease is important, as this will help doctors and
veterinarians more easily identify and diagnose positive cases. We apply
machine learning models to provide accurate and interpretable predictions of
Coccidioidomycosis. We assemble a set of radiographic images and use it to
train and test state-of-the-art convolutional neural networks to detect
Coccidioidomycosis. These methods are relatively inexpensive to train and very
fast at inference time. We demonstrate the successful application of this
approach to detect the disease with an Area Under the Curve (AUC) above 0.99
using 10-fold cross validation. We also use the classification model to
identify regions of interest and localize the disease in the radiographic
images, as illustrated through visual heatmaps. This proof-of-concept study
establishes the feasibility of very accurate and rapid automated detection of
Valley Fever in radiographic images.
