Paper Info
Title
Detection Of Covid-19 In X-Ray Images By Classification Of Bag Of Visual Words Using Neural Networks
Abstract
Coronavirus disease 2019 (COVID-19) was classified as a pandemic by the World Health Organization in March 2020. Given that this novel virus most notably affects the human respiratory system, early detection may help prevent severe lung damage, save lives, and help prevent further disease spread. Given the constraints on the healthcare facilities and staff, the role of artificial intelligence for automatic diagnosis is critical. The automatic diagnosis of COVID-19 based on medical images is, however, not straightforward. Due to the novelty of the disease, available X-ray datasets are very limited. Furthermore, there is a significant similarity between COVID19 X-rays and other lung infections. In this paper, these challenges are addressed by proposing an approach consisting of a bag of visual words and a neural network classifier. The proposed method can classify X-ray chest images into non-COVID-19 and COVID-19 with high performance. Three public datasets are used to evaluate the proposed approach. Our best accuracy on the first, second, and third datasets is 96.1, 99.84, and 98 percent. Since detection of COVID-19 is important, sensitivity is used as a criterion. The proposed method's best sensitivities are 90.32, 99.65, and 91 percent on these datasets, respectively. The experimental results show that extracting features with the bag of visual words results in better classification accuracy than the state-of-the-art techniques.
Year
DOI
Venue
2021
10.1016/j.bspc.2021.102750
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Keywords
DocType
Volume
COVID-19, Coronavirus, Bag of visual, Classifier
Journal
68
ISSN
Citations 
PageRank 
1746-8094
1
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Zahra Nabizadeh-Shahre-Babak110.37
Nader Karimi214532.75
Pejman Khadivi310.37
Roshanak Roshandel410.71
Ali Emami588.05
Shadrokh Samavi623338.99