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A Hybrid Pipeline for Covid-19 Screening Incorporating Lungs Segmentation and Wavelet Based Preprocessing of Chest X-Rays
Preprint
in English
| medRxiv
| ID: ppmedrxiv-22272311
ABSTRACT
We have developed a two-module pipeline for the detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 is a traditional convnet that generates masks of the lungs overlapping the heart and large vasa. Module 2 is a hybrid convnet that preprocesses CXRs and corresponding lung masks by means of the Wavelet Scattering Transform, and passes the resulting feature maps through an Attention block and a cascade of Separable Atrous Multiscale Convolutional Residual blocks to produce a class assignment as Covid or non-Covid. Module 1 was trained on a public dataset of 6395 CXRs with radiologist annotated lung contours. Module 2 was trained on a dataset of 2362 non-Covid and 1435 Covid CXRs acquired at the Henry Ford Health System Hospital in Detroit. Six distinct cross-validation models, were combined into an ensemble model that was used to classify the CXR images of the test set. An intuitive graphic interphase allows for rapid Covid vs. non-Covid classification of CXRs, and generates high resolution heat maps that identify the affected lung regions.
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Diagnostic study
/
Prognostic study
/
Rct
Language:
English
Year:
2022
Document type:
Preprint