A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems

The process of identifying covid-19 affected parts in lungs during RT-PC test and various tests available in healthcare sector always remains time consuming and costly process. There is no proper treatment available for covid-19 infections found on lungs. The time taken for initiating treatment after getting RTPC result may increase the risk of spreading the infection to another person and also severity level of the disease. The methods available in finding covid-19 are particularly focuses in finding out corona infections in lung rather finding subsequent diseases such as tuberculosis, asthma, lung cancer, emphysema, chronic diseases and Influenza in lungs. The computed tomography scanned images of a patient can carry footprint of all subsequent discussed diseases. The process of analyzing CT scan can be useful in finding the severity level of covid-19 accurately and helps in gathering information about subsequent disease in lungs based on infected parts found in lung CT images. This research article focuses on preparing a preprocessing strategy followed for finding out various severity levels in lung lesions. The collected CT images undergone basic preprocessing stage of image processing and taken for feature selection process. The basic strategy followed in preprocessing are image format conversion, data cleaning, cropping, resize and noise removal. The results obtained from this research work can be extended for analyzing infections in lungs based on their severity levels.