A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19

Abstract

The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early stage. In this study, a large margin piecewise linear classifier was developed to diagnose COVID-19 compared to a wide range of viral pneumonia, including SARS and MERS, using chest x-ray images. In the proposed method, a preprocessing pipeline was employed. Moreover, deep pre- and post-rectified linear unit (ReLU) features were extracted using the well-known VGG-Net19, which was fine-tuned to optimize transfer learning. Afterward, the canonical correlation analysis was performed for feature fusion, and fused deep features were passed into the LMPL classifier. The introduced method reached the highest performance in comparison with related state-of-the-art methods for two different schemes (normal, COVID-19, and typical viral pneumonia) and (COVID-19, SARS, and MERS pneumonia) with 99.39% and 98.86% classification accuracy, respectively.

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DOI

https://doi.org/10.1016/j.compbiomed.2021.104927

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https://napier-repository.worktribe.com/output/3735042/a-large-margin-piecewise-linear-classifier-with-fusion-of-deep-features-in-the-diagnosis-of-covid-19