Abstract S11-04: Using CXR-Net to detect COVID-19 and non-COVID-19 patients

A critical step in the fight against COVID-19 pandemic is the screening and rapid recognition of those affected by Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). The main screening method used for detecting COVID-19 infection is the reverse transcriptase-polymerase chain reaction (RT-PCR). Furthermore, the main screening methods for SARS-CoV-2 are chest X-ray (CXR) and computed tomography (CT). While CTs provide greater diagnostic accuracy, CXRs are more readily available and enable rapid triaging of patients in the most affected areas. Since CXR imaging is typically carried out as part of a standard procedure for patients with a respiratory complaint, it represents an ideal complement to RT-PCR testing. For this reason, much effort is ongoing into the development of Artificial Intelligence diagnostic systems based on Neural Networks (NN) that can aid radiologists in accurately interpreting CXRs in SARS-CoV-2 cases. Here we present CXR-Net, a two-module pipeline for SARS-CoV-2 detection. Module I is based on Res-CR-Net, a type of NN originally developed for the semantic segmentation of microscopy images (doi: 10.1088/2632-2153/aba8e8), which can process images of any size, retaining the original resolution of the input images in the feature maps of all layers and in the final output. Module I was trained on a dataset of 6395 Antero/Posterior CXRs with radiologist annotated lung contours to generate accurate masks of the lungs that overlap the heart and large vasa, and are minimally influenced by regions of consolidation or other texture alterations due to underlying pathologies. Module II 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). A particular advantage of this hybrid net is the removal of deep network instabilities associated with adversarial images (noise or small deformations in the input images that are visually insignificant, but that the network does not reduce correctly, leading to incorrect classification). This net converges more rapidly than an end-to-end learned architecture, does not suffer from vanishing or exploding gradients, and prevents overfitting, leading to better generalization. Module II takes as inputs the patients’ CXRs and corresponding lung masks calculated by Module I, and produces as outputs a class assignment (Covid or non-Covid) and high resolution heat maps that identify the SARS associated lung regions. In preliminary work, CXR-Net was tested on a small dataset of CXRs from non-Covid and RT-PCR confirmed Covid patients acquired at HFHS Hospital in Detroit. Module II was trained against 405 CXRs, and validated against 84 CXRs (validation statistics: accuracy = 0.810; precision = 0.833; recall = 0.932; F1 score = 0.88, ROC_auc = 0.942). Citation Format: Benjamen Huber, Abdulah Haikal, Domenico Gatti, Hamid Soltanian-Zadeh. Using CXR-Net to detect COVID-19 and non-COVID-19 patients [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr S11-04.