A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system - Singh, Law Kumar; Khanna, Munish; Garg, Hitendra; Singh, Rekha (Multimedia tools and applications, 2025)
The SARS-CoV-2 coronavirus strain’s introduction in December 2019 resulted in the development of the new coronavirus disease, COVID-19. Following its first appearance, the virus quickly spread throughout the world and is now considered to be a pandemic. There were 6,885,962 recorded deaths and 689,853,908 confirmed cases as of April 4, 2023. It is essential to put in place a thorough testing strategy in order to stop the disease from spreading. Nonetheless, a number of testing approaches are presently under consideration due to a restricted inventory and a limited supply of testing equipment. Recent expert analysis suggests that images from chest computed tomography (CT) scans could reveal important information about COVID-19, which is why we are using this modality as a focus of our investigation. Moreover, numerous recent studies have demonstrated that selecting the most informative features from the subject images improves the classification models’ efficiency and shortens the time needed for training and testing. All of this encourages us to present a study that suggests a novel, efficient, and fast feature selection system as the core of the proposed highly competent clinical decision support system for COVID-19 infection prediction. Using a publicly accessible CT image dataset, this four-phase system divides the images into two categories: "COVID-19-infected human" and "healthy human". Pre-processing is the first step in the study, after which features from different categories in the images are extracted in the next phase. In the third phase, the most influential features are then selected using three algorithms: the Teaching Learning-Based Optimization Algorithm (TLBO), the Cuckoo Search Optimization Algorithm (CSO), and a proposed hybrid of these two. To the best of the authors’ knowledge, these algorithms have rarely been applied to feature selection for COVID-19 infection prediction, which highlights the originality and inventiveness of the work. Following that, five machine learning (ML) classifiers that made use of the features selected during the feature selection stage are used to categorize the chest CT scans. Multiple tests, implementing the 70:30 approach, are then conducted for thorough investigation, and several performance-measuring metrics were computed during each test. A thorough investigation has been performed through multiple tests, and during each test, several performance-measuring metrics have been computed. Such in-depth investigations have rarely been published in state-of-the-art studies. With the suggested methodology, a noteworthy categorization accuracy of 97.94% is achieved. When compared to the other ML classifiers that were used, the XGBoost classifier has performed exceptionally well and produced the best results. In comparison to several recent studies, this suggested soft computing-based clinical system performs highly impressively in binary classification. This methodology has the potential to be implemented as a healthcare intervention in medical facilities worldwide, given its highly auspicious results. Additionally, it might be able to help radiologists who are overworked by offering an additional assessment. This effective system can help radiologists identify COVID-19 from medical images, which is a significant benefit in many remote locations in developing countries.