Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks

TL;DR

This study demonstrates that deep convolutional neural networks can accurately and rapidly detect pulmonary Coccidioidomycosis (Valley fever) in radiographic images, aiding diagnosis in veterinary and human medicine.

Contribution

The paper introduces a machine learning approach using CNNs for automated detection and localization of Valley fever in radiographs, achieving high accuracy with AUC above 0.99.

Findings

Achieved AUC > 0.99 in detection accuracy

Enabled rapid and interpretable diagnosis through heatmaps

Proved feasibility of automated disease detection in radiographs

Abstract

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.