@inproceedings{8126011491e2484bb691f8d1480a72e4,
title = "Attention based Covid-19 Detection using Generative Adversarial Network",
abstract = "The novel Coronavirus Disease 2019 (nCOVID-19) pandemic is a global health challenge, that requires collaborative efforts from multiple research communities. Effective screening of infected patients is a significant step in the fight against COVID-19, as radiological examination being an important screening methods. Early findings reveal that anomalies in chest X-rays of COVID-19 patients exist. As a result, a number of deep learning methods have been developed, and studies have shown that the accuracy of COVID-19 patient recognition using chest X-rays is very high. In this paper, we propose an attention based deep neural network for classifying the COVID-19 images, and extracting useful clinical information. Generative adversarial network is used to generate the synthetic COVID-19 images, as well as a good latent representation of both COVID-19 and normal images. Experiment results on public datasets shows the effectiveness of the proposed approach.",
keywords = "Covid, Discriminator, GANs, Generator, Keras, MobileNet, ResNet",
author = "Aiman Siddiqui and Asim Ahmed and Saleem, {Ali Faisal} and Alvi, {Zeshan Khan} and Tanvir Alam and Rizwan Qureshi",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th IEEE International Conference on Computing and Information Sciences, ICCIS 2021 ; Conference date: 29-11-2021 Through 30-11-2021",
year = "2021",
doi = "10.1109/ICCIS54243.2021.9676189",
language = "English",
series = "Proceedings - 2021 IEEE 4th International Conference on Computing and Information Sciences, ICCIS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Jilani, {Muhammad Taha}",
booktitle = "Proceedings - 2021 IEEE 4th International Conference on Computing and Information Sciences, ICCIS 2021",
address = "United States",
}