@article{Okolo_G-2022_98061,
title = {IEViT: an enhanced vision transformer architecture for chest X-ray image classification},
author = {Okolo, G. and Katsigiannis, S. and Ramzan, N.},
month = {sep},
year = {2022},
abstract = {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.},
volume = {226},
journal = {Computer Methods and Programs in Biomedicine},
publisher = {Elsevier},
url = {https://doi.org/10.1016/j.cmpb.2022.107141},
}
