COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer
- Authors
- Chattopadhyay, Soham; Dey, Arijit; Singh, Pawan Kumar; Geem, Zong Woo; Sarkar, Ram
- Issue Date
- Feb-2021
- Publisher
- MDPI
- Keywords
- COVID-19 detection; CGRO algorithm; deep features; meta-heuristic; feature selection; CT-scan; chest X-ray
- Citation
- DIAGNOSTICS, v.11, no.2
- Journal Title
- DIAGNOSTICS
- Volume
- 11
- Number
- 2
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80470
- DOI
- 10.3390/diagnostics11020315
- ISSN
- 2075-4418
- Abstract
- The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.
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