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COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Monday, Happy Nkanta Li, Jianping Nneji, Grace Ugochi Nahar, Saifun Hossin, Altab Jackson, Jehoiada Ejiyi, Chukwuebuka Joseph |
| Copyright Year | 2022 |
| Description | Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening. |
| Starting Page | 741 |
| e-ISSN | 20754418 |
| DOI | 10.3390/diagnostics12030741 |
| Journal | Diagnostics |
| Issue Number | 3 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-18 |
| Access Restriction | Open |
| Subject Keyword | Diagnostics Remote Sensing Chest X-ray (cxr) Covid-19 Convolutional Neural Network Multi-resolution Analysis Super Resolution Siamese Network |
| Content Type | Text |
| Resource Type | Article |
