Comparative Analysis of CLAHE and AHE on Application of CNN Algorithm in the Detection of Covid-19 Patients

In 2021, Covid-19 is no longer a new threat for people in Indonesia and the world. The virus that has spread since December 2019 has created many transformation in many aspects for society. Various detection tools are emerged continuously to support government in overcoming the Covid-19 pandemic. Numerous cases that continue to grow in community, certainly, also requires detection tools with the best performance to handle this pandemic. In the field of informatics, many researchers use chest X-ray images to detect Covid-19, as a practitioner in informatics, authors attempt to apply several algorithms to get the best performance from Covid-19 detection. The objective of this study is to apply the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) algorithms in the detection of Covid-19 using Convolutional Neural Network (CNN) algorithm with VGG19 model. The dataset used in this study was a total of 1000 chest X-ray images and 1000 normal chest X-ray images obtained through Kaggle. The results of this study show that application of CLAHE has the highest accuracy of 99% for Covid-19 detection using VGG19. It is proved that the application of Histogram Equalization is able to improve the detection performance.