Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.

最近，随着快速检测试剂盒数量有限，COVID-19 大流行急剧增加。因此，自动化 COVID-19 诊断模型对于从放射图像中识别疾病的存在至关重要。早期的研究主要集中在使用 X 射线图像诊断 COVID-19 的人工智能 (AI) 技术的发展上。本文旨在开发一种称为 DLMMF 的基于深度学习的多模态融合技术，用于从计算机断层扫描 (CT) 图像中进行 COVID-19 诊断和分类。所提出的 DLMMF 模型在三个主要过程上运行，即基于 Weiner Filtering (WF) 的预处理、特征提取和分类。所提出的模型融合了使用 VGG16 和 Inception v4 模型的深度特征。最后，基于高斯朴素贝叶斯 (GNB) 的分类器用于识别测试 CT 图像并将其分类为不同的类别标签。DLMMF 模型的实验验证是使用开源 COVID-CT 数据集进行的，该数据集总共包含 760 张 CT 图像。实验结果定义了卓越的性能，最大灵敏度为 96.53%，特异性为 95.81%，准确度为 96.81%，F分数为 96.73%。