Table 1.
Results of previous studies for Covid-19 and non-Covid-19 classification using X-ray images
| Study | Year | No. of Images | Methods | Test Methods | Results |
|---|---|---|---|---|---|
| Tuncer et al. [26] | 2020 | 321 images (87 Covid-19 and 234 Healthy) | Residual Exemplar Local Binary Pattern, Iterative Relief, Decision Tree, Linear Discriminant, Support Vector Machine, k-Nearest Neighborhood and Subspace Discriminant | 10-fold; 80% Train-20% Test; 50% Train-50% Test | Sen: 0,8755–0,9829/0,8297-0,9798/0,8149-1,0000; Spe: 0,9997-1,0000/0,9444-1,0000/0,9380-1,0000; Acc: 0,9663-0,9955/0,9130-0,9945/0,9049-0,9906 |
| Panwar et al. [27] | 2020 | 284 images (142 Covid-19 and 142 Healthy) | Convolutional Neural Network (nCOVnet) | 70% Train-30% Test | Sen: 0,9762; Spe: 0,7857; Acc: 0,881 |
| Ozturk et al. [28] | 2020 | 625 images (125 Covid-19 and 500 Healthy) | Convolutional Neural Network (DarkNet) | 5-fold | Sen: 0,9513; Spe: 0,953; Acc: 0,9808; F-1 Score: 0,9651 |
| Mohammed et al. [29] | 2020 | 50 images (25 Covid-19 and 25 Healthy) | Multi-Criteria Decision Making (Naive Bayes, Neural Network, Support Vector Machine, Radial Basis Function, k-Nearest Neighbors, Stochastic Gradient Descent, Random Forests, Decision Tree, AdaBoost, CN2 Rule Inducer Algorithm) | Unspecified | Sen: 0,706-0,974; Spe: 0,557-1,000; Acc: 0,620-0,987; F-1 Score: 0,555–0,987; AUC: 0,800-0,988; Time: 0,14–7,57 s. |
| Khan et al. [30] | 2020 | 594 images (284 Covid-19 and 310 Healthy) | Convolutional Neural Network (CoroNet (Xception)) | 4-fold | Sen: 0,993; Spe: 0,986; Acc: 0,990; F-1 Score: 0,985 |
| Apostolopoulos and Mpesiana [31] | 2020 | 728 images (224 Covid-19 and 504 Normal) | Transfer Learning with Convolutional Neural Networks (VGG19, MobileNet v2, Inception, Xception, Inception ResNet v2) | 10-fold | Sen: 0,9866; Spe: 0,9646; Acc: 0,9678 |
| Waheed et al. [32] | 2020 | 1.124 images (403 Covid-19 and 721 Healthy) | Convolutional Neural Network (VGG-16) and Synthetic Data Augmentation | Train: 932 (331 Covid-19 and 601 Healthy); Test: 192 (72 Covid-19 and 120 Healthy) | Sen: 0,69-0,90; Spe: 0,95-0,97; Acc: 0,85-0,95 |
| Mahmud et al. [33] | 2020 | 610 images (305 Covid-19 and 305 Healthy) | Transfer Learning with Convolutional Neural Networks (Stacked Multi-Resolution CovXNet) | 5-fold | Sen: 0,978; Spe: 0,947; Acc: 0,974; F-1 Score: 0,971; AUC: 0,969 |
| Vaid et al. [34] | 2020 | 545 images (181 Covid-19 and 364 Healthy) | Convolutional Neural Network (VGG-19) and Trainable Fully Connected Layers | Train: 348 (115 Covid-19 and 233 Healthy); Validation: 88 (32 Covid-19 and 56 Healthy); Test: 109 (34 Covid-19 and 75 Healthy) | Sen: 0,9863; Spe: 0,9166; Acc: 0,9633; F-1 Score: 0,9729 |
| Benbrahim et al. [35] | 2020 | 320 images (160 Covid-19 and 160 Healthy) | Transfer Learning with Convolutional Neural Networks (Inceptionv3 and ResNet50) | 70% Train-30% Test | Sen: 0,9803-0,9811; Acc: 0,9803-0,9901; F-1 Score: 0,9803-0,9901 |
| Elaziz et al. [36] | 2020 | Dataset-1: 1.891 images (216 Covid-19 and 1.675 Healthy); Dataset-2: 1.560 images (219 Covid-19 and 1.341 Healthy) | Fractional Multichannel Exponent Moments, Manta-Ray Foraging Optimization and KNN classifier | 80% Train-20% Test | Sen: 0,9875-0,9891; Acc: 0,9609-0,9809 |
| Martínez et al. [37] | 2020 | 240 images (120 Covid-19 and 120 Healthy) | Convolutional Neural Network (Neural Architecture Search Network (NASNet)) | 70% Train-30% Test | Sen: 0,97; Acc: 0,97; F-1 Score: 0,97 |
| Loey et al. [38] | 2020 | 148 images (69 Covid-19 and 79 Healthy) | Transfer Learning with Convolutional Neural Networks (Alexnet, Googlenet, and Resnet18) | Train: 130 (60 Covid-19 and 70 Healthy); Test: 18 (9 Covid-19 and 9 Healthy) | Sen: 1,000; Spe:1,000; Acc: 1,000 |
| Toraman et al. [39] | 2020 | 1.281 images (231 Covid-19 and 1.050 Healthy) | Convolutional Neural Network (CapsNet) | 10-fold | Sen: 0,28-0,9742; Spe:0,8095–0,98; Acc: 0,4914-0,9724; F-1 Score: 0,55-0,9724; Time: 16–500 s. (Note: The results show the average fold.) |
| Duran-Lopez et al. [40] | 2020 | 6.926 images (2.589 Covid-19 and 4.337 Healthy) | Convolutional Neural Network | 5-fold | Sen: 0,9253; Spe:0,9633; Acc: 0,9443; F-1 Score: 0,9314; AUC: 0,988 |
| Minaee et al. [41] | 2020 | 5.184 images (184 Covid-19 and 5.000 Healthy) | Transfer Learning with Convolutional Neural Networks (ResNet18, ResNet50, SqueezeNet, and DenseNet-121) | Train: 2.084 (84 Covid-19 and 2.000 Healthy); Test: 3.100 (100 Covid-19 and 3.000 Healthy) | Sen: 0,98; Spe:0,751-0,929 |