Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization
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 interpretat...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2020-09-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125944630/view |
| _version_ | 1818539070715854848 |
|---|---|
| author | Shui-Hua Wang Xiaosheng Wu Yu-Dong Zhang Chaosheng Tang Xin Zhang |
| author_facet | Shui-Hua Wang Xiaosheng Wu Yu-Dong Zhang Chaosheng Tang Xin Zhang |
| author_sort | Shui-Hua Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T21:37:12Z |
| format | Article |
| id | doaj.art-fa64689432584cc4871d40ce32643015 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T21:37:12Z |
| publishDate | 2020-09-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-fa64689432584cc4871d40ce326430152022-12-22T00:49:58ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-09-0113110.2991/ijcis.d.200828.001Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based OptimizationShui-Hua WangXiaosheng WuYu-Dong ZhangChaosheng TangXin ZhangCorona 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.https://www.atlantis-press.com/article/125944630/viewWavelet Renyi entropythree-segment biogeography-based optimizationfeedforward neural networkCOVID-19diagnosis |
| spellingShingle | Shui-Hua Wang Xiaosheng Wu Yu-Dong Zhang Chaosheng Tang Xin Zhang Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization International Journal of Computational Intelligence Systems Wavelet Renyi entropy three-segment biogeography-based optimization feedforward neural network COVID-19 diagnosis |
| title | Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization |
| title_full | Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization |
| title_fullStr | Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization |
| title_full_unstemmed | Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization |
| title_short | Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization |
| title_sort | diagnosis of covid 19 by wavelet renyi entropy and three segment biogeography based optimization |
| topic | Wavelet Renyi entropy three-segment biogeography-based optimization feedforward neural network COVID-19 diagnosis |
| url | https://www.atlantis-press.com/article/125944630/view |
| work_keys_str_mv | AT shuihuawang diagnosisofcovid19bywaveletrenyientropyandthreesegmentbiogeographybasedoptimization AT xiaoshengwu diagnosisofcovid19bywaveletrenyientropyandthreesegmentbiogeographybasedoptimization AT yudongzhang diagnosisofcovid19bywaveletrenyientropyandthreesegmentbiogeographybasedoptimization AT chaoshengtang diagnosisofcovid19bywaveletrenyientropyandthreesegmentbiogeographybasedoptimization AT xinzhang diagnosisofcovid19bywaveletrenyientropyandthreesegmentbiogeographybasedoptimization |