Table 1.
Research findings and limitations using Deep Learning frameworks
| Objective | Dataset | Time Frame | Source of Dataset | Methods | Model performance | Limitation |
|---|---|---|---|---|---|---|
| To forecast of the future of covid-19 cases [26] | Total 346 people from five countries | January 20, 2020 to April 4, 2020 | Not available | i) ARIIMA and wavelet-based technique ii) Regression Tree | Predictive model | – |
| To Predict the trends and stopping time of covid-19 outbreak [27] | Not available | Till march 31,2020 | Two universities | LSTM networks | End by June 2020 | – |
| Predicting trend of covid-19 in China [5] | Current Covid-19 and 2003 SARS epidemic data | Not available | National Health Commision of China | SEIR and LSTM | Peak of 4,000 daily infections between February 4 to 7, 2020 in China | Limited number of factors are considered |
| Pandemic prediction [29] | Confirmed covid-19 cases | Not available | (i) Johns Hopkins University, (ii) WHO, (iii) Ding Xiang Yuana | SEIR | Reach peak in late May 2020 | – |
| Forecasting of covid-19 in China [31] | Confirmed covid-19 cases | (i) News Networks (ii) WHO | Modified stacked auto-encoder | Predictive model | – | |
| Detection of covid-19 cases using chest X-ray images [11] | 13,975 CXR images | Not available | COVIDx dataset | COVID-Net | Predictive model | Not a production ready solution |
| To determine the uncertainity and interpretability[6] | 5,941 PA chest radiography images | Not available | (i) Dr. Joseph Cohen’s Github repository and (ii) Kaggle’s Chest X-Ray Images | Bayesian Deep Learning | Predictive model | Study is only limited to estimating uncertainty in already developed models |
| Detection of coronavirus disease using X-ray images [32] | 100 chest X-ray images | Not available | GitHub | Convolutional neural networks | Accuracy : 98% | Small dataset |
| covid-19 outbreak prediction in India [13] | Covid-19 cases in India | January 30th 2020 to March 30th 2020 | Johns Hopkins University | SEIR and Regression Model | RMSLE : 1.75 | Limited data |
| Prediction of country wise risk of covid-19 [33] | Confirmed covid-19 cases | January 22 2020 to March 10 2020 | Not available | LSTM networks | Accuracy: 78% | Model achieved less accuracy |
| Forecasting of covid-19 pandemic [82] | Not available | January 21, 2020 to April 02, 2020 | Sourceb | Machine Learning | 97% confidence interval | Small Dataset was taken for Senegal |
| Screening for covid-19 disease using CT images [1] | 453 CT images | Not available | Not available | Deep Learning | AUC: 0.90 | Training dataset is small |
| Detection for covid-19 from chest CT using weak label [3] | 630 CT images | December 13, 2019 to February 6, 2020 | Not available | DeCoVNet | AUC: 0.959 | Dataset from single hospital |
| Automatic detection of covid-19 from X-ray images [34] | 2,870 X-ray images | Not available | Public medical repositories | Deep Learning, Transfer Learning | Accuracy : 96.78% | Small sample of positive cases |
| Prediction of the pandemic trend of covid-19 in Italy [35] | Daily reports of covid-19 | Jan 22,2020 to Apr 02,2020 | Johns Hopkins University | Extended susceptible-infected-removed | 95% CI | Asymptomatic and unconfirmed cases may be ignored. |
| Identification of covid-19 through mobile phone based survey [12] | Not available | Not available | Not available | AI algorithms | Difficult to collect data that is used for this model | |
| Detection of covid-19 using Artificial Intelligence [36] | 260 chest X-ray images | Not available | (i) GitHub, and (ii) Kaggle | Convolutional neural network | Accuracy: 100% | Small dataset was taken to validate model |
| Predicting outbreak trend of coronavirus disease in India [37] | China’s covid-19 cases | Jan 22, 2020 to April 3, 2020 | Kaggle | Machine Learning | Forecasting prediction for India | Limited to Indian context |
| Predicting the trends in covid-19 outbreak in Iran [38] | Iran’s covid-19 data | Feb 15, 2020 to Mar 18, 2020 | (i) WorldOmeter website, and (ii) Google trends | LSTM and Linear Regression | RMSE : 7.562 | Limited Google Search data |
| Analysis of confirmed cases using AI [83] | Not available | Not available | Binary Classification and Regression model | Accuracy : 95.7% | Study only takes weather parameters in consideration | |
| AI model to distinguish covid-19 cases [39] | 4,356 3D chest CT images | Not available | Six medical centers | Transfer Learning | AUC : 0.96 | Train and Test from same dataset. |
| covid-19 case detection [40] | 127 X-ray | Not available | ChestX-ray8 database | Deep Learning | Accuracy : 98.09% | Limited number of covid-19 X-ray images. |
| AI System for covid-19 [43] | 6,752 CT scans images | Not available | CC-CCII | Deep Learning | Accuracy : 92.49% | |
| Diagnosis of coronavirus disease from X-ray images [7] | 5,949 posteroanterior chest radiography images | Not available | COVIDx | COVIDiagnosis-Net | Accuracy : 98.3% | Model require Fine-tuned hyperparameters for accurate predictions |
| Accurate model for covid-19 prediction [44] | 605 real-world data | Not available | Not available | Deep Learning | Accuracy : 94.5% | Limited dataset |
| Time dependent SIR model for covid-19 [45] | Not available | Jan 15,2020 to Mar 2,2020 | NHC | SIR model | Day-wise prediction | Limited domain |
| Lung infection quantification [46] | 549 CT images | Not available | Not available | Deep Learning | Similarity coefficients : 91.6% | Validation was conducted on same dataset |
| Large scale screening method [47] | 2,685 CT images | Not available | Three medical source from China | Random Forest | Accuracy :87.9% | only baseline CT findings of covid-19 patients were included in study |
| To diagnose COIVD-19 patient [48] | 50 chest X-ray images | Not available | Not available | Transfer Learning | F1-score : 0.89 | Small dataset |
| Pneumonia screening [49] | 43,583 chest CT images | Not available | (i) X-VIRAL, and (ii) X-COVID | Deep Learning | AUC : 0.836 | High false negative rate |
| Classification of covid-19 cases [50] | 196 samples of CXRs | Not available | Japanese Society of Radiological Technology | Deep Learning | Accuracy : 95.12% | Limted training data. |
| Diagnosis of covid-19 [51] | 349 covid-19 CT images | Not available | COVID-CT-Dataset | Multi-task learning | F1-score : 0.90 | Limited number of CT images |
| Classification model [52] | 5,856 CT images | Not available | Not available | Transfer Learning | Accuracy :96% | |
| covid-19 diagnosis method using X-ray [54] | 170 X-ray images and 361 CT images | Not available | Multiple Sources | Deep Learning | Accuracy : 98% | Small dataset. |
| Detection of covid-19 cases [56] | 5,863 X-ray images | Not available | Not available | Transfer Learning | Accuracy : 99% | Used only 624 images |
| Identification of abnormal CT patterns [57] | 9,749 chest CT images | Not available | Not available | Deep Learning | Pearson correlation coefficient : 0.92 | Model was trained only with specific abnormalities data |
| Identification of covid-19 using chest X-ray [58] | 455 chest X-ray images | Not available | Multiple Sources | Transfer Learning | Accuracy : 91.24% | Small number of cases are considered |
| covid-19 patient detection using CT images [59] | 109 confirmed cases from China | Not available | Not available | Transfer Learning | AUC : 0.948 | Small dataset |
| Identification of covid-19 cases from X-ray images [60] | 94,323 frontal view chest X-ray images | Not available | NIH CXR dataset | Deep Learning | Accuracy : 95.7% | Pre-training is required for the model to achieve high accuracy |
| Estimation of global covid-19 spread [62] | Not available | Not available | Chinese National Health Commission | Neural network | Analysis based model | |
| To evaluate pneumonia cases during the covid-19 [2] | 5,863 chest X-ray images | Not available | Not available | Transfer Learning | False negatives : 0.7% | low number of covid-19 chest X-ray images in dataset |
| covid-19 diagnosis based on chest X-ray [63] | 15,959 CXR images | Not available | Multiple Sources | Transfer Learning | F1-score : 0.945 | Limited amount of CXR images |
| covid-19 patterns detection through X-ray images [64] | 13,800 X-ray images | Not available | Multiple Sources | Deep artificial neural networks | Accuracy : 93.9% | Less heterogeneous dataset was used |
| covid-19 infection detection [65] | 60 3D CT lung scans | Not available | TCIA dataset | Convolutional neural network | Accuracy : 96.2% | Small dataset |
| To develop covid-19 diagnosis system [66] | 144,167 CT images | Not available | COVID-CS | Transfer Learning | Sensitivity : 95.0% | – |
| covid-19 Detection using CXRs [10] | Not available | Not available | Multiple Sources | Transfer Learning | Accuracy : 99.01% | Limited number of covid-19 pneumonia CXR data |
| To predict covid-19 from chest X-ray images [14] | 5,071 chest X-ray images | Not available | (i) Chestxray-Dataset, and (ii) ChexPert dataset | Transfer Learning | Specificity : 97.8% | Dataset contained less than 100 covid-19 X-ray images |
| covid-19 detection [68] | 100 axial CT images | Not available | covid-19 CT segmentation dataset | Deep Learning | Analysis based model | Small dataset |
| Diagnosis of covid-19 with chest X-ray images | Transfer Learning [70] | 1,076 posteroanterior CXR (PCXR) images | Not available | i) covid-19 Image, and ii) RSNA dataset | Accuracy : 96% | – |
| Estimating covid-19 trend in Spain [71] | Not available | Not available | Sourcec | Bayesian Poison-Gamma model | Predictive model | – |
| covid-19 pneumonia severity predicting model [72] | 94 posteroanterior CXR images | Not available | Multiple Sources | DenseNet model | MAE: 0.78 | Small number of samples |
| Diagnosis of coronavirus using CT images [74] | 88 chest CT scans images | Not available | Not available | Details Relation Extraction neural network | AUC : 0.99 | Small dataset |
| AI system for covid-19 diagnosis [75] | 10,250 CT images | Not available | Multiple Sources | Deep Learning based AI system | AUC : 0.971 | Some limitations arises when unbalanced dataset is used |
| covid-19 diagnosis based on CT scans [76] | 349 CT scan images | Jan 19th to Mar 25th | (i) medRxiv, and (ii) bioRxiv | Transfer Learning | F1-score : 0.85 | Not robust |
| CT imaging classification and segmentation for covid-19 [77] | 1,044 CT images | Not available | (i) COVID-CT data, and (ii) Sourced | MTL architecture | AUC : 0.93 | Limited covid-19 images |
| covid-19 lung infection segmentation model using CT images [78] | 100 axial CT images | Not available | CT Segmentation dataset | Transfer Learning | Sensitivity : 0.870 | Limited dataset |
| covid-19 diagnostic and prognostic analysis [79] | 5,372 CT images | Not available | Not available | Deep Learning | AUC : 0.90 | Many factors are not considered |
| CT images feature analysis to screen covid-19 [80] | 51 CT images | Not available | Kaggle database | Naive Bayes | Accuracy : 96.07% | Small dataset |
| covid-19 predicitons using X-ray Images [81] | 3,905 X-ray images | Not available | Not available | Convolutional neural network | Accuracy : 99.18% | Used limited dataset |
aChinese government authorized website
bhttps://www.tableau.com/covid-19-coronavirus-data-resources
chttps://covid19.isciii.es/resources/serie_historica_acumulados.csv