An Approach to Detect COVID-19 Disease from CT Scan Images using CNN - VGG16 Model
K. Sai Prasad, S. Pasupathy, P. Chinnasamy, A. Kalaiarasi
Abstract
As a result of the outbreak, an unusual virus spread event has occurred, threatening human safety worldwide. To prevent infections from spreading quickly, large numbers of people must be screened. Rapid Test and RT-PCR are common testing tool for regular testing that is used to test all covid affected users. However, the increasing number of false positives has paved the way for the investigation of alternative test methods for corona virus effected patients' chest X-rays have shown to be an effective alternate predictor for testing if an individual is affected with COVID-19 virus. However, consistency is, once again, dependent on radiological experience. A diagnostic decision support device that assists the physician in evaluating the victims' lung scans can alleviate the doctor's medical workload. Machine Learning Techniques, specifically Convolutional Neural Networks (CNN) VGG16 model is used to train dataset and use trained model to predict, have been developed in this project. Four distinct deep CNN architectures are tested on photographs of chest X-rays for treatment of COVID-19. The collection of data sets of covid 19 X-ray imageries and non-covid 19 X-ray imageries are used to train the model and test its accuracy. CNN-based architectures were discovered to be capable of diagnosing COVID-19 disease.