Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual’s health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

由于冠状病毒 (COVID-19) 的爆发不断在世界范围内蔓延，科学家们一直在设计各种技术，主要集中在人工智能上，以了解这种流行病的困难。在当前的全球紧急情况下，临床企业正在寻找新的进展来筛查和对抗 COVID-19 污染。人工智能使用的策略可以拉伸筛查感染的传播，区分高度感染的患者，并在持续监督疾病方面具有说服力。通过充分剖析过去患者的信息，人工智能的预期可以进一步用于传递危险。国际患者支持以及人口检测、医疗护理、通知和感染控制方面的建议可以帮助对抗这种致命病毒。我们提出了混合深度学习方法来诊断 COVID-19。此处使用分层方法来测量患者的症状水平，并分析患者图像数据是否他/她对 COVID-19 呈阳性。这项工作利用智能 AI 技术，通过大浦智能环在 24 小时内快速预测和诊断冠状病毒。在实验室中，借助使用RNN和CNN算法的深度学习模型准备冠状病毒快速测试，以快速准确地诊断冠状病毒。结果显示值为 0 或 1。结果 1 表示此人感染了冠状病毒，结果 0 表示此人未感染冠状病毒。这里考虑 X 射线和 CT 图像分类，以便利用阈值来识别个人从初始阶段到严重阶段的健康状况。阈值0.5用于识别冠状病毒初始状态，1用于识别患者的冠状病毒重症。所提出的方法用于四个加权参数，以减少假阳性和假阴性图像分类结果，从而快速准确地诊断 COVID-19。