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Conference 12033 > Paper 12033-11
Paper 12033-11

Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs

In person: 21 February 2022 • 2:00 PM - 2:20 PM PST

Abstract

The purpose of this study is to devise a Computer Aided Diagnosis (CAD) system that is able to detect COVID-19 abnormalities from chest radio-graphs with increased efficiency and accuracy. We investigate a novel deep learning based ensemble model to classify the category of pneumonia from chest X-ray images. We use a labeled image dataset provided by Society for Imaging Informatics in Medicine for a kaggle competition that contains chest radio-graphs. And the task of our proposed CAD is to categorize between negative for pneumonia or typical, indeterminate, atypical for COVID-19. The training set (with labels publicly available) of this dataset contains 6334 images belonging to 4 classes. Furthermore, we experiment on the efficacy of our proposed ensemble method. Accordingly, we perform a ablation study to confirm that our proposed pipeline drives the classification accuracy higher and also compare our ensemble technique with the existing ones quantitatively and qualitatively.

Presenter

Univ. of Maryland, Baltimore (United States)
Presenter/Author
Univ. of Maryland, Baltimore (United States)
Author
Univ. of Maryland, Baltimore County (United States)
Author
Univ. of Maryland, Baltimore County (United States)
Author
Univ. of Maryland, Baltimore County (United States)
Author
David Chapman
Univ. of Maryland, Baltimore County (United States)
Author
National Institutes of Health Clinical Ctr. (United States)
Author
National Institutes of Health Clinical Ctr. (United States)
Author
Univ. of Maryland, Baltimore County (United States)