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- Title
Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis.
- Authors
Ding, Hui; Fan, Lingyan; Zhang, Jingfeng; Gao, Guosheng
- Abstract
Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. The proposed system employed convolutional neural networks (CNNs) for classification, specifically EfficientNet and ResNet architectures. The data underwent preprocessing steps, including image resizing, Gaussian noise addition, and data augmentation. The dataset was divided into training, validation, and test sets. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability. Results: The EfficientNet-based models outperformed the ResNet-based models across all metrics. The highest accuracy achieved was 99.44% for CXR images and 99.81% for CT images with EfficientNetB5. The models also demonstrated high precision, recall, and F1 scores. For statistical significance, the p-values were less than 0.05, indicating that the results are significant. Conclusion: Integrating CXR and CT images using deep learning significantly improves the accuracy of COVID-19 diagnosis. The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.
- Subjects
COVID-19 testing; COMPUTED tomography; QUESTIONNAIRES; MEDICAL care; ACCELERATION (Mechanics); CHEST X rays; LUNGS; PATIENT care; DIAGNOSTIC errors; DESCRIPTIVE statistics; DEEP learning; ARTIFICIAL neural networks; EPIDEMICS; DIGITAL image processing; COVID-19; PHYSIOLOGICAL effects of acceleration
- Publication
British Journal of Hospital Medicine (17508460), 2024, Vol 85, Issue 8, p1
- ISSN
1750-8460
- Publication type
Article
- DOI
10.12968/hmed.2024.0244