Diagnostics, Vol. 13, Pages 1268: Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

Diagnostics, Vol. 13, Pages 1268: Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

Diagnostics doi: 10.3390/diagnostics13071268

Authors: Saleh Almuayqil Sameh Abd El-Ghany Abdulaziz Shehab

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.