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

Abstract

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.

Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

“…Apart from that, another approach to secure data communication involves the privacy preservation model under the data sanitisation process. In this sanitisation process, the key is optimally tuned by using many advanced AI-based algorithms, which include nature-inspired algorithms and DL techniques [205,206,232]. Moreover, the privacy-preserving model or the authentication mechanism can be designed according to the secure key management, which commonly involves secret key encryption or public key infrastructure with certifcates [219,220].…”