TY - JOUR
AU - Okolo, Gabriel Iluebe
AU - Katsigiannis, Stamos
AU - Ramzan, Naeem
PY - 2022
DA - September
TI - IEViT: an enhanced vision transformer architecture for chest X-ray image classification
JO - Computer Methods and Programs in Biomedicine
VL - 226
DO - https://doi.org/10.1016/j.cmpb.2022.107141
AB - Background and Objective: Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification.
PB - Elsevier
UR - https://uws.pure.elsevier.com/en/publications/bb36a643-b843-4bcc-b0a1-727cd7da06ef
KW - Coronavirus (COVID-19)
ER