Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

Al-Asfoor, Muntasir and Abed, Mohammed Hamzah (2022) Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images. Lecture Notes in Networks and Systems, 350. 161 – 170.

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Abstract

During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI-based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest X-ray images to detect the infected subjects. An enhanced convolutional neural network (CNN) architecture has been designed with 22 layers which are then trained over a chest X-ray dataset. More after, a classification component has been introduced to classify the X-ray images into three categories. The system has been evaluated through a series of observations and experimentations. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed COVID-19 with accuracy of 0.9961 and normal subjects with accuracy of 0.96067 while it showed 0.9588 accuracy on Pneumonia. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type:	Article
Additional Information:	Cited by: 2; Conference name: 15th International Conference on Information Technology and Applications, ICITA 2021; Conference date: 13 November 2021 through 14 November 2021; Conference code: 277069; All Open Access, Green Open Access
Depositing User:	RED Unit Admin
Date Deposited:	13 Mar 2025 12:17
Last Modified:	13 Mar 2025 12:17
URI:	https://bnu.repository.guildhe.ac.uk/id/eprint/19675

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