The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.

由于不同变体的出现，由冠状病毒引起的 COVID-19 疾病不断变化，全球每天都有成千上万的人死亡。早期发现这种新型肺部疾病可以降低死亡率。在本文中，开发了一种基于机器学习 （ML） 和深度学习 （DL） 的自动化方法，使用从三个公开可用的数据集（总共 11,407 张图像;7397 张 COVID-19 图像和 4010 张正常图像）中提取的计算机断层扫描 （CT） 扫描图像来检测 COVID-19。已经提出了一种无监督聚类方法，这是一种改进的基于区域的聚类技术，用于分割 COVID-19 CT 扫描图像。此外，已采用 contourlet 变换和卷积神经网络 （CNN） 从分割的 CT 扫描图像中单独提取特征，并将它们融合在一个特征向量中。二元差分进化 （BDE） 方法已被用作一种特征优化技术，以从融合特征向量中获得可理解的特征。最后，考虑了装袋技术的基于 ML/DL 的集成分类器被用于从 CT 图像中检测 COVID-19。A fivefold 和泛化交叉验证技术已用于验证目的。还对几个预训练模型（AlexNet、ResNet50、GoogleNet、VGG16、VGG19）进行了分类实验，发现具有融合特征的集成分类器技术提供了最先进的性能，准确率高达 99.98%。