At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.

目前，5G 蜂窝和物联网 (IoT) 技术的巨大进步在医疗保健领域的不同应用中非常有用。同时，COVID-19 通常从动物传播到人，但今天它通过调整结构在人与人之间传播。这是一种严重的病毒，不恰当地导致了全球大流行。放射科医生利用 X 射线或计算机断层扫描 (CT) 图像来诊断 COVID-19 疾病。通过使用图像处理技术对疾病进行识别和分类至关重要。因此，需要一种新的智能疾病诊断模型来识别 COVID-19。在这个观点中，本文提出了一种新的支持物联网的深度可分离卷积神经网络 (DWS-CNN)，它具有深度支持向量机 (DSVM)，用于 COVID-19 诊断和分类。所提出的 DWS-CNN 模型旨在通过结合一组过程（即数据采集、基于高斯滤波 (GF) 的预处理、特征提取和分类）来检测二元和多类 COVID-19。最初，患者数据将在数据采集阶段使用物联网设备收集并发送到云服务器。此外，GF技术用于去除图像中存在的噪声。然后，使用 DWS-CNN 模型代替默认卷积进行自动特征提取。最后，应用 DSVM 模型来确定 COVID-19 的二元和多类标签。DWS-CNN 模型的诊断结果针对胸部 X 射线 (CXR) 图像数据集进行了测试，并根据不同的性能指标对结果进行了研究。