Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets

TL;DR

This paper identifies biases in COVID-19 chest X-ray datasets and proposes a two-stage methodology involving bias detection and image enhancement techniques to improve deep learning classification accuracy.

Contribution

It introduces a novel two-stage approach to analyze and mitigate dataset bias in COVID-19 X-ray classification using advanced image processing and CNN models.

Findings

Bias in publicly available datasets confirmed through experiments.

Image augmentation techniques improve classification accuracy.

Achieved 90.47% accuracy on 3-class COVID-19 detection.

Abstract

Since the onset of the COVID-19 pandemic in 2020, millions of people have succumbed to this deadly virus. Many attempts have been made to devise an automated method of testing that could detect the virus. Various researchers around the globe have proposed deep learning based methodologies to detect the COVID-19 using Chest X-Rays. However, questions have been raised on the presence of bias in the publicly available Chest X-Ray datasets which have been used by the majority of the researchers. In this paper, we propose a 2 staged methodology to address this topical issue. Two experiments have been conducted as a part of stage 1 of the methodology to exhibit the presence of bias in the datasets. Subsequently, an image segmentation, super-resolution and CNN based pipeline along with different image augmentation techniques have been proposed in stage 2 of the methodology to reduce the effect of bias. InceptionResNetV2 trained on Chest X-Ray images that were augmented with Histogram Equalization followed by Gamma Correction when passed through the pipeline proposed in stage 2, yielded a top accuracy of 90.47% for 3-class (Normal, Pneumonia, and COVID-19) classification task.