Image Pre-processing techniques comparison : COVID-19 detection through Chest X-Rays via Deep Learning

Abstract

The COVID-19 pandemic had a particularly devastating effect, spreading rapidly over the world and infecting about 36 million individuals. Chest radiography is a critical component that aids in the early detection of a variety of diseases. With the spread of the pandemic, training Convolutional Neural Networks (CNN) to detect and identify COVID-19 from chest X-rays is becoming more popular. However, there are few publicly available and medically validated datasets for COVID-19 infected chest X-Rays, resulting in the model failing to generalize successfully. It is critical to pre-process and enrich the data used to train the model in order to achieve this aim. Global Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Top Bottom Hat Transform are some of the pre-processing techniques available. In this study, we examine and compare all of these pre-processing methods in order to determine which is best for building a CNN model that can accurately classify an image as infected with COVID-19 or Viral Pneumonia.