Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans

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 diagnosed with chest CT scan. This work investigates opportunities for applying machine learning solutions for automated detection and localization of COVID-19 on chest CT scan images. Chest CT scan image is consisting three various stages like, COVID and Non COVID. In COVID and Non COVID report consisting COVID-19 patient grey scale images and Normal patient grey scale images. Chest CT scan images are usually used to identify the causes of patients’ 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 patient based on COVID and Non COVIDation CT scan report. This report is classified by COVID-19 and Normal patient.