Quantitative and Qualitative Comparison Study between Classification Methods on normal and abnormal (with a coronavirus ) lung images

Classification is concerned with establishing criteria that can be used to identify or distinguish different populations of objects that appear in images. In this paper Supervised and unsupervised classification method applied on normal, abnormal (with a coronavirus) ct- lung images (which it took from Al shaikh zaeid Hospital)  to study the quantitative and qualitative properties of these two categories. The analysis of performance with default quantitative parameters revealed that (kurtosis, skewness, entropy, Stander deviation (STD), mean). We found that: Qualitative (as seen) of   abnormal lung images after applying  Supervisors classification are better than the qualitative of abnormal lung images after applying  unsupervisors classification to detect the virus with white color in the lower lobes of the lung.. from The quantitative Properties such as (kurtosis, skewness) of original lung images are similar in rising to resulted value after applying  Supervisors classification on it, so Supervisors method is better than unSupervisors method to distinguishing between normal and abnormal lung images.