Detection of Covid-19 in Chest X-ray Image using CLAHE and Convolutional Neural Network
Buyut Khoirul Umri, Muhammad Wafa Akhyari, Kusrini Kusrini
Abstract
In 2019, the COVID-19 virus has spread to various parts of the world including Indonesia. This global pandemic becomes a lethal outbreak since there is no vaccine to treat or prevent transmission of the virus. Rapid Test is selected as an essential method to detect Covid-19 in Indonesia because the price is fairly cheap compared to the SWAB test. The increase in Covid-19 patients tends to lead to limited capacity for the Covid-19 test available at the hospital so that the latest technology to detect and overcome this pandemic issue is needed. Thus, the present research aims to examine the total of 100 X-Ray chest images of the Covid-19 patients and 100 X-ray normal chest images. The application of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) methods are implemented to analyze the dataset with two scenarios in obtaining the detection results. The results of this research reveal that the application of CLAHE is likely to affect Covid-19 detection accuracy using CNN. Also, the application of the CNN basic model shows significant results compared to the application of VGG16 transfer learning.