The research paper was published in and selected for the front cover of the Journal of Imaging.
By Mr John Stevenson (Senior Communications Officer), Published
In his recent research paper (co-authored with MSc in AI student Azucena Ascencio-Cabral), Dr Constantino Carlos Reyes-Aldasoro (medical imaging and data visualisation specialist in City's giCentre), evaluates the performance of five deep learning architectures in classifying COVID-19, Pneumonia and Healthy Individuals with computed tomography and proposed a framework for a fair comparison of these architectures.
One of the classifiers he used is the Residual Network (ResNet) - a deep learning model which is used for computer vision applications.
Front cover of journal
Other classifiers used were ResNet-50r, DenseNet-121, MobileNet-v3 and the state of the art CaiT-24-XXS-224 (CaiT) transformer.
The research paper, Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography, was published in, and selected as the front cover for the Journal of Imaging.
Computed tomography (CT) is a medical imaging procedure using special x-ray equipment to acquire detailed three-dimensional scans of organs inside the body.