A New Optimal Diagnosis System for Coronavirus (COVID-19) Diagnosis Based on Archimedes Optimization Algorithm on Chest X-Ray Images

The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia. Correct detection of this disease can help to avo...

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Vydáno v:Computational intelligence and neuroscience Ročník 2021; číslo 1; s. 7788491
Hlavní autoři: Chen, Liping, Rezaei, Tahereh
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Hindawi 2021
John Wiley & Sons, Inc
Témata:
Accuracy
Algorithms
Artificial intelligence
Chest
Classification
Coronaviruses
COVID-19
Datasets
Deep Learning
Diagnosis
Diagnostic imaging
Diagnostic systems
Epidemics
Equalization
Feature extraction
Heuristic methods
Histograms
Humans
Image quality
Infections
Infectious diseases
International organizations
Machine learning
Mathematical optimization
Medical imaging
Medical imaging equipment
Neural Networks, Computer
Optimization
Optimization algorithms
Patients
Reproducibility of Results
Respiratory diseases
SARS-CoV-2
Viral diseases
Viruses
X-Rays
China
United States > US
ISSN:1687-5265, 1687-5273, 1687-5273
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Popis
Shrnutí:The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia. Correct detection of this disease can help to avoid its spreading increasingly. In this paper, a new CAD-based approach is suggested for the optimal diagnosis of this disease from chest X-ray images. The proposed method starts with a min-max normalization to scale all data into a normal scale, and then, histogram equalization is performed to improve the quality of the image before main processing. Afterward, 18 different features are extracted from the image. To decrease the method difficulty, the minimum features are selected based on a metaheuristic called Archimedes optimization algorithm (AOA). The model is then implemented on three datasets, and its results are compared with four other state-of-the-art methods. The final results indicated that the proposed method with 86% accuracy and 96% precision has the highest balance between accuracy and reliability with the compared methods as a diagnostic system for COVID-19.
Bibliografie:ObjectType-Article-1
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Academic Editor: Navid Razmjooy
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2021/7788491