Heatmap Template Generation for COVID-19 Biomarker Detection in Chest X-rays

Mirtha Lucas, Miguel Lerma, Jacob Furst, Daniela Raicu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis. Given the relatively small number of available Covid-19 X-ray images and the need to make progress in understanding the disease, we propose a transfer learning technique applied to a pretrained VGG19 neural network to build a deep convolutional model capable of detecting four possible conditions: normal (healthy), bacteria, virus (not Covid-19), and Covid-19. The transformation of the multi-class deep learning output into binary outputs and the detection of Covid-19 image patterns using Grad-CAM technique show promising results. The discovered patterns are consistent across images from a given class of disease and constitute explanations of how the deep learning model makes classification decisions. In the long run, the identified patterns can serve as biomarkers for a given disease in chest X-ray images.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages438-445
Number of pages8
ISBN (Electronic)9781728195742
DOIs
StatePublished - Oct 2020
Externally publishedYes
Event20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States
Duration: Oct 26 2020Oct 28 2020

Publication series

NameProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Conference

Conference20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
CountryUnited States
CityVirtual, Cincinnati
Period10/26/2010/28/20

Keywords

  • Artificial Intelligence
  • Biomarkers
  • Covid-19
  • Neural Networks

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Molecular Biology
  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Modeling and Simulation
  • Health Informatics

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