Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of ‘SARS-CoV-2’ in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of ‘COVID-19’.

在“COVID-19”造成的全球大流行和灾难中，每个研究机构和科学家都在尽最大努力发明或寻找治疗该疾病的疫苗或药物。本研究的目的是设计和开发一种基于深度学习的多模式，用于使用胸片和基因组序列筛查 COVID-19。该模式还可有效地发现严重急性呼吸系统综合症 - 冠状病毒 2 和其他流行病毒（如严重急性呼吸系统综合症 - 冠状病毒、中东呼吸综合症 - 冠状病毒、人类免疫缺陷病毒和人类 T 细胞）之间的基因组相似程度白血病病毒。国家生物技术信息中心提供的数据集的实验结果，GitHub，和 Kaggle 存储库显示，它成功地检测到宿主基因组中的“SARS-CoV-2”基因组，准确率达到 99.27%，并将胸片筛查为 COVID-19、非 COVID 肺炎和健康，灵敏度为95.47%。因此，它可能被证明是医生快速分类受感染和未感染基因组的有用工具。它还有助于从现有药物中寻找最有效的药物来治疗“COVID-19”。