Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images
Article
Pathan, S., Siddalingaswamy, P.C., Kumar, Preetham, Pai, Manohara MM, Ali, Tanweer and Acharya, U. Rajendra. 2021. "Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images." Computers in Biology and Medicine. 137. https://doi.org/10.1016/j.compbiomed.2021.104835
| Article Title | Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Pathan, S., Siddalingaswamy, P.C., Kumar, Preetham, Pai, Manohara MM, Ali, Tanweer and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 137 |
| Article Number | 104835 |
| Number of Pages | 14 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2021.104835 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482521006296 |
| Abstract | The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application. |
| Keywords | BGWO; Covid-19 ; CNN; DST; Ensemble; GWO; WOA; Hyperparameters |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Manipal Academy of Higher Education, India |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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