CLAHE based Enhancement to Transfer Learning in COVID-19 Detection

Abstract

Early diagnosis of COVID-19 disease becomes possible with the enhancements on feature learning and advanced pre-processing stages for classification of chest X-ray images using deep learning. Besides, high-performance models have been developed by many researchers due to the popularity of Deep Learning. In this study, chest X-ray images were pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) before the classification with particular popular transfer learning approaches in deep learning architectures including AlexNet, MobileNet, VGG16, and DarkNet19. The originality of the paper is pre-processing the images using CLAHE to obtain more significant representations of airways and pathologies instead of training with raw chest X-ray images. The best CLAHE parameters were determined considering the results of various trials at a specified range. The other superior contribution of the proposal is using a large-scale dataset, which is comprised of 3500 healthy and 3615 chest x-rays with COVID-19. The CLAHE-based transfer learning proposal achieved an accuracy rate of 95.878% as the most successful binary classification result for COVID-19 and healthy using VGG16 model and CLAHE parameters including disk value of 56, clip-limit of 0.2.