EXPLORING THE IMPACT OF IMAGE ENHANCEMENT AND DATA AUGMENTATION TECHNIQUES ON LUNG DETECTION IN CHEST RADIOGRAPHY IMAGES

ABSTRACT

The health and wellbeing of people all over the world are being severely impacted by the ongoing COVID-19 pandemic. One of the most important ways to check for COVID-19 is chest radiography, so ensuring that infected people undergo this test is crucial. This research set out to assess the efficacy of various image enhancement and data augmentation techniques for use with digital chest X-Rays in the detection of COVID-19 patients. White-balance correction (WB) and contrast-limited adaptive histogram equalization (CLAHE) were the two methods used to improve the images. These two technologies have also been applied to examine this impact on COVID-19 discrimination. Also, Data was augmented in two distinct ways, using a different set of techniques and combining it with image enhancement techniques. Transfer learning was used to compare image classification models pre-trained on the ImageNet dataset to well-known deep learning architectures. Our models were evaluated and compared using the novel-combined chest X-Ray datasets. We observed that the VGG-16 model outperforms other models with an accuracy of 98% when image WB and CLAHE are used together. Due to their superior performance, these pre-trained models can greatly improve the speed and accuracy of COVID-19 diagnosis. © 2023, International Organization on 'Technical and Physical Problems of Engineering'. All rights reserved.