Table 1.
Comparing the discussed DL-COVID-19 strategies and their characteristics.
| Authors | Main idea | Advantage | Disadvantage | TL used? | Security mechanism? | Simulation environment |
|---|---|---|---|---|---|---|
| Alshazly, Linse [9] | New deep network topologies and a TL technique are developed using custom-sized input and a TL approach. | - Attained average accuracies of 99.4% and 92.9%, respectively, and sensitivity scores of 99.8% and 93.7%. | -High complexity -Security is not considered |
Yes | No | Python |
| Fung, Liu [42] | Proposing a model incorporating generative adversarial image inpainting, focus loss, and a lookahead optimizer. | -High accuracy -High robustness |
-High energy consumption -High delay -Security is not considered |
No | No | Python Radiomic package PyRadiomics |
| Kundu, Basak [43] | Creating a COVID-19 detection system that categorizes CT scan images of the lungs into binary instances. | -High accuracy -Moderate energy consumption |
-Poor robustness -Security is not considered |
Yes | No | Python |
| Owais, Yoon [44] | Creating a method for separating COVID-19 negative and positive patients from chest radiographic images such as CT scans and X-rays. | -The X-ray and CT scan datasets have an average F1 score (F1) of 94.60% and 95.94%, respectively, and an AUC of 97.50% and 97.99%. | -Poor robustness High energy consumption -High delay |
Yes | No | MATLAB |
| Abdel-Basset, Chang [45] | Demonstrating a novel semi-supervised few-shot segmentation technique for effective lung CT image segmentation. | -High accuracy -Moderate accuracy |
-High complexity -Security is not considered |
No | No | Python-Tensorflow |
| Scarpiniti, Ahrabi [46] | Providing a strategy that uses a deep denoising convolutional autoencoder's compact and meaningful hidden representation. | -High reliability -Low computational cost -High accuracy |
-Security is not considered | No | No | TensorFlow and Keras |
| Khan, Alhaisoni [47] | Presenting a DL model optimization approach based on parallel fusion and optimization | -High accuracy -High performance |
-Following feature selection and feature fusion, the number of redundant features that remain | Yes | No | MATLAB2020b |
| Rehman, Zia [48] | Offering a framework for detecting a variety of chest illnesses, including COVID-19 | -Proper accuracy -High predictability |
-Low robustness | Yes | No | Not mentioned |
| Ours | Proposing a COVID-19 detection blockchain with a lightweight CNN-enabled blockchain that uses 3D CNNs. | -High accuracy -High precision -High security -High scalability |
-High delay | Yes | Yes | Python Keras |