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CXR-Net is a two-module Artificial Intelligence pipeline for the quickdetection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 was trained on apublic dataset of 6395 CXRs with radiologist annotated lung contours togenerate masks of the lungs that overlap the heart and large vasa. Module 2 isa hybrid convnet in which the first convolutional layer with learnedcoefficients is replaced by a layer with fixed coefficients provided by theWavelet Scattering Transform (WST). Module 2 takes as inputs the patients CXRsand corresponding lung masks calculated by Module 1, and produces as outputs aclass assignment (Covid vs. non-Covid) and high resolution heat maps thatidentify the SARS associated lung regions. Module 2 was trained on a dataset ofCXRs from non-Covid and RT-PCR confirmed Covid patients acquired at the HenryFord Health System (HFHS) Hospital in Detroit. All non-Covid CXRs were frompre-Covid era (2018-2019), and included images from both normal lungs and lungsaffected by non-Covid pathologies. Training and test sets consisted of 2265CXRs (1417 Covid negative, 848 Covid positive), and 1532 CXRs (945 Covidnegative, 587 Covid positive), respectively. Six distinct cross-validationmodels, each trained on 1887 images and validated against 378 images, werecombined into an ensemble model that was used to classify the CXR images of thetest set with resulting Accuracy = 0.789, Precision = 0.739, Recall = 0.693, F1score = 0.715, ROC(AUC) = 0.852.

CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays