GLCM Based LGBM classification for COVID-19 Analysis using Chest X-ray Images

The novel corona virus (COVID-19) has turned out to be the biggest challenge of 21 century. Since, it is spreading at a very high pace all over the world, fast and accurate detection of this virus becomes a necessity. However, the human annotation of images is time-consuming; it is not a good strategy for dealing with big amounts of medical imaging data. This work looks at the experimental examination of features that are well-suited for examining X-ray pictures in COVID19. This investigation encompasses the series of steps, including data augmentation, pre-processing, feature extraction using GLCM followed by feature selection using PCA, and finally classification is performed by Light Gradient Boosting Machine. The proposed method was validated by comparing it to COVID-19 X-ray dataset, with an accuracy achieved is 92.40%.