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Abstract: COVID-19 is a bacterial, viral or fungal infection of one or both sides of the lungs that causes lung alveoli to fill up with fluid or pus, which is usually diagnose with chest CT scan. Chest CT scan image consists of three various stages like, COVID and Non COVID. Chest CT scan images are usually used to identify the causes of patient’s symptoms, including the classes of lung or heart disorders. This study aims to propose an Iterated Function System (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CT images and to improve screening accuracy.Find the accuracy of COVID-19 patients based on COVID and Non COVIDation CT scan reports. The medical field is the most sensitive of all the domains ever known, for a simple reason that it deals with humans and advances in this field is a matter of pride for the entire human race. It can be detected by analyzing chest CT scan. Analyzing chest CT scan is a difficult task and requires precision. We aim at designing a highly efficient system to predict if a user suffers from COVID-19 by analyzing the patient’s chest CT scan images and increasing the accuracy of the system by use of a machine.