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

Author name : ABDELAZIZ IBRAHIM ABDELKHALEK SHEHAB
Publication Date : 2023-10-30
Journal Name : Diagnostics

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 Models

Publication Link

https://www.mdpi.com/2075-4418/13/7/1268

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