Binary Classification of COVID-19 CT Images Using CNN
Shankar Shambhu, Deepika Koundal, Prasenjit Das, Chetan Sharma
Abstract
COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.
Topics & Concepts
Coronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Pandemic2019-20 coronavirus outbreakBinary classificationComputed tomographyArtificial intelligenceComputer scienceMedicineComputer visionDiseaseRadiologyPathologyInfectious disease (medical specialty)Support vector machineOutbreakCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingAI in cancer detection