Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

K Shankar; Sachi Nandan Mohanty; Kusum Yadav; T Gopalakrishnan; Ahmed M Elmisery

doi:10.1007/s11571-021-09712-y

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Cogn Neurodyn. 2023 Jun;17(3):1-14. doi: 10.1007/s11571-021-09712-y. Epub 2021 Sep 10.

Authors

K Shankar¹, Sachi Nandan Mohanty², Kusum Yadav³, T Gopalakrishnan⁴, Ahmed M Elmisery⁵

Affiliations

¹ Teresina, Brazil Federal University of Piauí.
² Hyderabad, India Department of Computer Science and Engineering, Vardhaman College of Engineering (Autonomous).
³ Hail, Saudi Arabia College of Computer Science and Engineering, University of Ha'il.
⁴ Vellore, India School of Computer Science and Engineering, Vellore Institute of Technology.
⁵ Pontypridd, UK Faculty of Computing, Engineering and Science, University of South Wales.

PMID: 34522236
PMCID: PMC8431962 (available on 2024-06-01)
DOI: 10.1007/s11571-021-09712-y

Abstract

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Keywords: COVID-19; Classification; Deep learning; Fusion model; Optimal feature selection.