Covid-19 Detection Using CT Scan based on Gray Level Co-Occurrence Matrix

Ameer, Aseel Qassim Abdul; Mohammed, Raghad Falih

The Coronavirus pandemic is one of the biggest problems the world has faced in the 21st century and this virus is a virus that infects the lung and causes breathing problems. In this research the program is designed for the purpose of reading images of the type CT scan, this study used 654 case these cases split in to two classes (infect , not infect), there are two phases in this study, training phase and testing phase. After training the training data store in database, the second phase is testing at first is pre-processing step which increase contrast, then remove lung by labelling the most contrast connected pixels and subtract labelling pixels from original image, the next step is noise removal by applying three filters (mean, median, Gaussian), after that applying gray level co-occurrence matrix (GLCM) in four directions (0°,45°,90° and 135°), then extract features from GLCM, in this study 10 features was extracted from each GLCM matrix, then compare between testing features and training database to specify the case is infect or not, in this study get accuracy 94% for detect the location of infection and detect the lung is infect or not.