Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images

TL;DR

This paper introduces Twin Augmentation, a novel technique that enhances pre-trained deep learning models for COVID-19 chest X-ray classification, especially in imbalanced datasets, and provides a realistic benchmark dataset for evaluation.

Contribution

It presents a new Twin Augmentation method to improve pre-trained models on imbalanced COVID-19 X-ray data and offers a comprehensive, heterogeneous benchmark dataset for fair evaluation.

Findings

Twin Augmentation significantly improves classification performance.

The benchmark dataset reveals limitations of existing models.

Twin Augmentation boosts accuracy without re-training models.

Abstract

The gold standard for COVID-19 is RT-PCR, testing facilities for which are limited and not always optimally distributed. Test results are delayed, which impacts treatment. Expert radiologists, one of whom is a co-author, are able to diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can facilitate timely treatment. Such diagnosis is particularly valuable in locations lacking radiologists with sufficient expertise and familiarity with COVID-19 patients. This paper has two contributions. One, we analyse literature on CXR based COVID-19 diagnosis. We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results. We compile and analyse a viable benchmark dataset from multiple existing heterogeneous sources. Such a benchmark is important for realistically testing models. Our second contribution relates to learning from imbalanced data. Datasets for COVID X-Ray classification face severe class imbalance, since most subjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural Networks (Twin NN) have, in recent years, emerged as effective ways of handling skewed data. We introduce a state-of-the-art technique, termed as Twin Augmentation, for modifying popular pre-trained deep learning models. Twin Augmentation boosts the performance of a pre-trained deep neural network without requiring re-training. Experiments show, that across a multitude of classifiers, Twin Augmentation is very effective in boosting the performance of given pre-trained model for classification in imbalanced settings.