A Comprehensive Study on Classification of COVID-19 on Computed Tomography with Pretrained Convolutional Neural Networks

Tuan D. Pham

202014 citationsDOI Open Access PDF

Abstract

This study presents an investigation on sixteen pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results update CNNs that achieve very high performance on the classification task and discover that implementation of transfer learning with direct input of whole image slices and without the use of data augmentation provide better classification results than the use of data augmentation. {\it Conclusions:} The findings alleviate the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models, and can facilitate the rapid deployment of AI tools to contain the spread of the coronavirus disease.

Topics & Concepts

Convolutional neural networkCoronavirus disease 2019 (COVID-19)Task (project management)Transfer of learningComputer scienceArtificial intelligenceComputed tomographyDeep learningArtificial neural networkPattern recognition (psychology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakImage (mathematics)Contextual image classificationMachine learningMedicineDiseasePathologyRadiologyEngineeringInfectious disease (medical specialty)OutbreakSystems engineeringCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education