Nature-inspired solution for coronavirus disease detection and its impact on existing healthcare systems

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy.

Automated summary

This paper investigates the application of machine learning and nature-inspired algorithms to brain MRI images of COVID-19 patients, proposing a model named MLNI-COVID-19. The model improves the classification and optimization of MRI images for better diagnosis, especially in the neglected area of brain images. The proposed model shows better performance in terms of sensitivity, specificity, and accuracy compared to existing algorithms.