Evolving Deep Ensembles For Detecting Covid-19 In Chest X-Rays

Since its outbreak reported in late 2019 in Wuhan, China, the novel coronavirus disease (COVID-19) has been the major challenge across the globe, affecting virtually all aspects of our lives. To effectively manage the pandemic, we need fast, non-invasive, and precise routines for detecting active CO...

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Bibliographic Details
Main Authors: Bosowski, Piotr, Bosowska, Joanna, Nalepa, Jakub
Format: Conference Proceeding
Language:English
Subjects:
classification ensembles
COVID-19
COVID-19 detection
Deep learning
genetic algorithm
Image processing
Inference algorithms
Measurement
Pandemics
Predictive models
X-ray
Online Access:Request full text
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Summary:Since its outbreak reported in late 2019 in Wuhan, China, the novel coronavirus disease (COVID-19) has been the major challenge across the globe, affecting virtually all aspects of our lives. To effectively manage the pandemic, we need fast, non-invasive, and precise routines for detecting active COVID-19 cases. Although there exist deep learning approaches for detecting COVID-19 in medical image data, their generalization abilities remain unknown. We tackle this issue and introduce deep ensembles that benefit from a wide range of architectural advances, alongside a new fusing approach to deliver accurate predictions. Also, we evolve their content to not only accelerate the inference but also to boost the classification performance. Our experiments, performed on a number of datasets of chest X-ray images, show that the proposed technique renders high-quality classification and generalizes well over a variety of test scans.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506119