  CXR-Net is a two-module Artificial Intelligence pipeline for the quick
detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 was trained on a
public dataset of 6395 CXRs with radiologist annotated lung contours to
generate masks of the lungs that overlap the heart and large vasa. Module 2 is
a hybrid convnet in which the first convolutional layer with learned
coefficients is replaced by a layer with fixed coefficients provided by the
Wavelet Scattering Transform (WST). Module 2 takes as inputs the patients CXRs
and corresponding lung masks calculated by Module 1, and produces as outputs a
class assignment (Covid vs. non-Covid) and high resolution heat maps that
identify the SARS associated lung regions. Module 2 was trained on a dataset of
CXRs from non-Covid and RT-PCR confirmed Covid patients acquired at the Henry
Ford Health System (HFHS) Hospital in Detroit. All non-Covid CXRs were from
pre-Covid era (2018-2019), and included images from both normal lungs and lungs
affected by non-Covid pathologies. Training and test sets consisted of 2265
CXRs (1417 Covid negative, 848 Covid positive), and 1532 CXRs (945 Covid
negative, 587 Covid positive), respectively. Six distinct cross-validation
models, each trained on 1887 images and validated against 378 images, were
combined into an ensemble model that was used to classify the CXR images of the
test set with resulting Accuracy = 0.789, Precision = 0.739, Recall = 0.693, F1
score = 0.715, ROC(AUC) = 0.852.
