Nowadays, with the rapid spread of Coronavirus disease (COVID-19) across the globe, the necessity to develop an intelligent system for early diagnosis and detection the COVID-19 infectious disease increases. In recent researches, Chest Xray (CXR) of individual lungs became a common method to identify COVID-19 virus. Manual interpretation of the CXR images can be a lengthy process and subjective to human errors. In this paper, a hybrid Deep Learning model called ReXception is implemented, trained, and evaluated using two types of datasets; Mutliclass and Binary. The network is evaluated based on its overall accuracy, loss, precision, and recall, in addition to the running time and network size. The results show positive indications of the network's performance, especially when compared to other state-of-the-art networks.


The accepted author manuscript is available on the University of Strathclyde repository under the following licence: https://purl.org/strath-1. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Cite as

Aburaed, N., Al-Saad, M., Panthakkan, A., al Mansoori, S., Al-Ahmad, H. & Marshall, S. 2022, 'A hybrid rexception network for COVID-19 classification from chest X-ray images', 2021 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Proceedings, pp. 1-5. https://doi.org/10.1109/ICECS53924.2021.9665598

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Last updated: 16 June 2022
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