RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images
Article
El-Dahshan, El-Sayed. A, Bassiouni, Mahmoud. M, Hagag, Ahmed, Chakrabortty, Ripon K, Loh, Huiwen and Acharya, U.Rajendra. 2022. "RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images." Expert Systems with Applications. 204. https://doi.org/10.1016/j.eswa.2022.117410
| Article Title | RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images |
|---|---|
| ERA Journal ID | 17852 |
| Article Category | Article |
| Authors | El-Dahshan, El-Sayed. A, Bassiouni, Mahmoud. M, Hagag, Ahmed, Chakrabortty, Ripon K, Loh, Huiwen and Acharya, U.Rajendra |
| Journal Title | Expert Systems with Applications |
| Journal Citation | 204 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0957-4174 |
| 1873-6793 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2022.117410 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0957417422007527 |
| Abstract | Since the advent of COVID-19, the number of deaths has increased exponentially, boosting the requirement for various research studies that may correctly diagnose the illness at an early stage. Using chest X-rays, this study presents deep learning-based algorithms for classifying patients with COVID illness, healthy controls, and pneumonia classes. Data gathering, pre-processing, feature extraction, and classification are the four primary aspects of the approach. The pictures of chest X-rays utilized in this investigation came from various publicly available databases. The pictures were filtered to increase image quality in the pre-processing stage, and the chest X-ray images were de-noised using the empirical wavelet transform (EWT). Following that, four deep learning models were used to extract features. The first two models, Inception-V3 and Resnet-50, are based on transfer learning models. The Resnet-50 is combined with a temporal convolutional neural network (TCN) to create the third model. The fourth model is our suggested RESCOVIDTCNNet model, which integrates EWT, Resnet-50, and TCN. Finally, an artificial neural network (ANN) and a support vector machine were used to classify the data (SVM). Using five-fold cross-validation for 3-class classification, our suggested RESCOVIDTCNNet achieved a 99.5 percent accuracy. Our prototype can be utilized in developing nations where radiologists are in low supply to acquire a diagnosis quickly. |
| Keywords | COVID-19 diagnosis; X-ray Lung images ; Pre-trained CNN methods; Inception-V3 & Resnet-50 ; TCN; EWT |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Ain Shams University, Egypt |
| Egyptian E-Learning University (EELU), Egypt | |
| Benha University, Egypt | |
| University of New South Wales | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan | |
| Ngee Ann Polytechnic, Singapore |
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https://research.usq.edu.au/item/z1v84/rescovidtcnnet-a-residual-neural-network-based-framework-for-covid-19-detection-using-tcn-and-ewt-with-chest-x-ray-images
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