Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images. (March 2022)

• Record Type: Journal Article
• Authors: Subhalakshmi, RT; Balamurugan, S Appavu alias; Sasikala, S
• Is Part Of: Concurrent engineering, research and applications. Volume 30:Number 1(2022)
• Page Start: 116
• Page End: 127
• Publication Date: 2022-03
• Subjects: deeplearning -- COVID-19 -- weiner filtering -- convolutional neural network -- Gaussian Naïve Bayes -- Deep learning multimodal fusion; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X211021435
• Languages: English
• ISSNs: 1063-293X

Abstract

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%.