IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

• Published in: International journal of machine learning and cybernetics Vol. 12; no. 11; pp. 3235 - 3248
• Main Authors: Le, Dac-Nhuong, Parvathy, Velmurugan Subbiah, Gupta, Deepak, Khanna, Ashish, Rodrigues, Joel J. P. C., Shankar, K.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Classification; Cloud computing; Complex Systems; Computational Intelligence; Computed tomography; Control; COVID-19; Data acquisition; Datasets; Diagnosis; Engineering; Feature extraction; Image processing; Internet of Things; Mechatronics; Medical imaging; Neural networks; Original; Original Article; Pattern Recognition; Robotics; Support vector machines; Systems Biology; Viral diseases; COVID-19; Deep learning; Multilabel classification; Feature extraction; Convolutional neural network
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-020-01248-7

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.