Table 1.
Comparative study of literature review
| Authors [citations] | Year | Methodology | Challenges | Accuracy |
|---|---|---|---|---|
| Santanu Roy et al., [27] | 2021 | Autoregressive Integrated Moving Average model | This method suitable only for the time series data | - |
| GruravDhimanaet al., [12] | 2021 | Deep learning-based multi-objective optimization method | The diagnosis of COVID-19 using the J48 approach needs further improvement. | 98.54% |
| Tulin Ozturk et al., [23] | 2020 | DarkCovidNet framework | This method does not support the dataset with more images | 98.08% |
| Dilbag Singh et al., [29] | 2020 | Convolutional Neural Network | This method does not support the larger and more complex dataset | - |
| Apostolopoulos I. D et al., [4] | 2020 | convolutional neural network | This method required more information for accurate classification which leads to time consumption. | 96.78% |
| Md. Zabirul Islam et al., [15] | 2020 | CNN-LSTM network | This method does not provide a better result when compared with radiologists | 99.4% |
| Vruddhi Shah et al., [28] | 2021 | Convolutional Neural Network | This method requires further improvement | 94.52% |
| Shashank Vaid et al., [19] | 2020 | convolutional neural networks | For instance, research aimed at estimating the number of individuals who could be infected by the virus but show no symptoms does not take into consideration the existence of "invisible cases" in this approach. | 96.3% |
| S. Tabik et al., [25] | 2020 | COVID-SDNet methodology | More CXR images from various hospitals cannot be handled by this method. | 97.72 |
| Guangyu Guo et al., [14] | 2021 | IE-Net | This approach is unable to process clinical detection data or medical images of various modalities. | 92.79% |
| Afshar Shamsi et al., [20] | 2021 | Transfer Learning-Based Classification | This method requires further improvement | - |
| Karen Panetta et al., [24] | 2021 | shape-dependent Fibonacci-p patterns-based feature descriptor | A 3D feature descriptor that can aid in the analysis of 3D medical images is not supported by this approach. | 98.44% |
| Shanjiang Tang et al., [30] | 2021 | EDL-COVID | For COVID-19 CXR images that have not been seen, this approach is unable to deliver high accuracy. | 95% |
| Shunjie Dong et al., [13] | 2021 | RCoNet | This method requires further improvement | - |
| Abdelkader Dairi et al., [10] | 2021 | Unsupervised VAE-Based 1SVM Detector | This method does not support for lager dataset. | - |