Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

Saleh Almuayqil; Sameh Abd El-Ghany; Abdulaziz Shehab

doi:10.3390/diagnostics13071268

Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

Diagnostics (Basel). 2023 Mar 28;13(7):1268. doi: 10.3390/diagnostics13071268.

Authors

Saleh Almuayqil¹, Sameh Abd El-Ghany^{1

2}, Abdulaziz Shehab^{1

2}

Affiliations

¹ Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
² Department of Information Systems, Mansoura University, Mansoura 35516, Egypt.

Abstract

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.

Keywords: COVID-19; deep learning; healthcare; multimodality; transfer learning.

Grants and funding

CV-35-41/This research was funded by JOUF UNIVERSITY, grant number CV-35-41