IoT enabled depth-wise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification
The paper presents a depth-wise separable convolution neural network (DWS-CNN) with deep support vector machine (DSVM) for COVID-19 diagnosis. Since radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19, in fact, it is essential the use of image processing techniques to identify and classify the disease. First, gaussian filtering is applied to remove the noise that exists in images, then, the DWS-CNN model is employed for automatic features’ extraction. Finally, the DSVM model is applied to determine the classes’ labels. The model is tested on Chest X-ray (CXR) image dataset and the experiment results show an accuracy of 98% for binary class case and 99% for multiclass case.
Data Analytics, Machine Learning
Le, D.N. and Parvathy, V. S. and Gupta, D. and Khanna, A. and Rodrigues, J. and Shankar, K., IoT enabled depth-wise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification, International Journal of Machine Learning and Cybernetics 12.11 (2021): 3235-3248.
Author of the review:
University of Genoa
You have to login to leave a comment. If you are not registered click here