COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

• Published in: Diagnostics (Basel) Vol. 12; no. 3; p. 741
• Main Authors: Monday, Happy Nkanta, Li, Jianping, Nneji, Grace Ugochi, Nahar, Saifun, Hossin, Md Altab, Jackson, Jehoiada, Ejiyi, Chukwuebuka Joseph
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.03.2022; MDPI
• Subjects: Artificial intelligence; chest X-ray (CXR); convolutional neural network; Coronaviruses; COVID-19; multi-resolution analysis; Neural networks; Siamese network; super resolution; COVID-19; Siamese network; chest X-ray (CXR); super resolution; multi-resolution analysis; convolutional neural network
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12030741

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.