CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images

Smitha Sunil Kumaran Nair; Leena R David; Abdulwahid Shariff; Saqar Al Maskari; Adhra Al Mawali; Sammy Weis; Taha Fouad; Dilber Uzun Ozsahin; Aisha Alshuweihi; Abdulmunhem Obaideen; Wiam Elshami

doi:10.1016/j.jmir.2024.03.046

CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images

J Med Imaging Radiat Sci. 2024 Apr 8:S1939-8654(24)00100-0. doi: 10.1016/j.jmir.2024.03.046. Online ahead of print.

Affiliations

¹ Department of Computing and Electronics Engineering, Middle East College, Sultanate of Oman. Electronic address: smitha@mec.edu.om.
² Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates.
³ Department of Postgraduate Studies, University of Dar es Salaam, Tanzania.
⁴ Department of Computing and Electronics Engineering, Middle East College, Sultanate of Oman.
⁵ Quality Assurance and Planning, German University of Technology (GUtech), Sultanate of Oman.
⁶ University Hospital, Sharjah, United Arab Emirates.

PMID: 38594085
DOI: 10.1016/j.jmir.2024.03.046

Abstract

Introduction: Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images.

Methods: The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images.

Results: The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates.

Conclusion: The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability.

Keywords: CNN; COVID-19; Chest X-Rays; Deep learning.