Impact of Local Histogram Equalization on Deep Learning Architectures for Diagnosis of COVID-19 on Chest X-rays

Abstract

Deep Learning (DL) is one of the most popular Machine Learning (ML) algorithms with feature learning capabilities. Its use is becoming widespread day by day due to its high-performance in classification in various fields, including medical image processing. DL is inspired by an advanced neural network structure and includes many parameters. In consequence of its high performance, it is used in the classification of many diseases. DL algorithms, which are frequently used in image processing, classify the pixels on the images by convolutional progress in different layers. Before learning the significant pixels in supervised ways, it can be ensured that the classification is more successful with different pre-processing methods. In this study, the effect of DL architectures on COVID19 was investigated using Local Histogram Equalization (LHE). Chest x-ray images were examined withand without-LHE to determine the impact of disk factor on transfer learning. The dataset consists of COVID-19, Pneumonia, and normal chest x-ray images. Chest xrays were segmented into two parts of right lung lobe and the left lung lobe. The effect on the classification performance of transfer learning was observed by applying different disk value for LHE. The experiments were evaluated on the different pretrained DL architectures, such as VGG16, AlexNet, and Inception model.