COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19
Sharif Hasani, Hamid Nasiri
Abstract
Following the COVID-19 pandemic, scientists have been looking for different ways to diagnose COVID-19, and these efforts have led to a variety of solutions. One of the common methods of detecting infected people is chest radiography. In this paper, an Automated Detection System using X-ray images (COV-ADSX) is proposed, which employs a deep neural network and XGBoost to detect COVID-19. COV-ADSX was implemented using the Django web framework, which allows the user to upload an X-ray image and view the results of the COVID-19 detection and image's heatmap, which helps the expert to evaluate the chest area more accurately.
Topics & Concepts
Coronavirus disease 2019 (COVID-19)UploadComputer scienceDeep learningArtificial intelligenceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakRadiographyArtificial neural networkPandemicImage (mathematics)Computer visionMedicineVirologyRadiologyWorld Wide WebPathologyDiseaseOutbreakInfectious disease (medical specialty)COVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingAI in cancer detection