TY - JOUR
T1 - Diagnosis of covid-19 by wavelet renyi entropy and three-segment biogeography-based optimization
AU - Wang, Shui Hua
AU - Wu, Xiaosheng
AU - Zhang, Yu Dong
AU - Tang, Chaosheng
AU - Zhang, Xin
N1 - Publisher Copyright:
© 2020 The Authors. Published by Atlantis Press B.V.
PY - 2020
Y1 - 2020
N2 - Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.
AB - Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.
KW - COVID-19
KW - Diagnosis
KW - Feedforward neural network
KW - Three-segment biogeography-based optimization
KW - Wavelet Renyi entropy
UR - http://www.scopus.com/inward/record.url?scp=85091598551&partnerID=8YFLogxK
U2 - 10.2991/ijcis.d.200828.001
DO - 10.2991/ijcis.d.200828.001
M3 - Article
AN - SCOPUS:85091598551
SN - 1875-6891
VL - 13
SP - 1332
EP - 1344
JO - International Journal of Computational Intelligence Systems
JF - International Journal of Computational Intelligence Systems
IS - 1
ER -