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.

冠状病毒是一种传染性危及生命的疾病，主要通过感染者咳嗽、打喷嚏或呼气传播。这种疾病是一项全球性挑战，需要先进的解决方案来解决这种大流行病对健康和福祉的多个方面。已经提出了不同类型的基于医疗和技术的解决方案来控制和治疗 COVID-19。机器学习是磁共振成像 (MRI) 分类中使用的技术之一，而自然启发算法也被用于图像优化。在本文中，我们将机器学习和自然启发算法结合起来用于 COVID-19 患者的大脑 MRI 图像，即冠状病毒的机器学习和自然启发模型（MLNI-COVID-19）。该模型改进了 MRI 图像分类和优化，以实现更好的诊断。该模型将提高整体性能，尤其是由于数据集不可用而被忽略的大脑图像区域。COVID-19 对患者大脑有严重影响。所提出的模型将有助于改进诊断过程，以实现更好的医疗决策和绩效。所提出的模型使用现有算法进行评估，并在灵敏度、特异性和准确性方面取得了更好的性能。