Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

Turki Althaqafi; Abdullah S Al-Malaise Al-Ghamdi; Mahmoud Ragab

doi:10.3390/healthcare11091204

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

Healthcare (Basel). 2023 Apr 22;11(9):1204. doi: 10.3390/healthcare11091204.

Authors

Turki Althaqafi¹, Abdullah S Al-Malaise Al-Ghamdi^{1

2}, Mahmoud Ragab^{3

4}

Affiliations

¹ Information Systems Department, HECI School, Dar Al-Hekma University, Jeddah 34801, Saudi Arabia.
² Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
³ Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁴ Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt.

Abstract

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

Keywords: COVID-19 diagnosis; chest X-rays; deep learning; quantum neural network; sine cosine algorithm.

Grants and funding

This research project was funded by the Vice Presidency for Graduate Studies, Business, and Scientific Research (GBR) at Dar Al-Hekma University, Jeddah, under grant no. (RFC/22-23/001).