Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models.
Saleh AlmuayqilSameh Abd El-GhanyAbdulaziz ShehabPublished in: Diagnostics (Basel, Switzerland) (2023)
In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time.
Keyphrases
- deep learning
- coronavirus disease
- sars cov
- artificial intelligence
- convolutional neural network
- machine learning
- end stage renal disease
- dual energy
- high resolution
- computed tomography
- ejection fraction
- air pollution
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- respiratory syndrome coronavirus
- big data
- palliative care
- risk factors
- magnetic resonance
- positron emission tomography
- image quality
- mass spectrometry
- optical coherence tomography