Table 1.
Comparative chart of the prior studies
| Paper title | Selected DataSet and its size | Models Applied | Best Model Justified |
|---|---|---|---|
| Butt et al. [20] | 618 transverse section CT samples |
ResNet 23 and self-crafted Self-Crafted ResNet-18-based CNN model |
ResNet 23 and self-crafted |
| Das et al. [21] | Approximately public 627 images [28] | Inceptionnet V3 Alexnet,Resnet50 VGGNet,CNN,Deep CNN and Xccetion-based self-crafted model | Deep CNN and Xccetion-based self-crafted model |
| Alakus et al. [22] | 18 laboratory findings of the 600 patients |
ANN,CNN,LSTM RNN,CNNLSTM CNNRNN |
CNNLSTM |
| Ardakani et al. [23] | Private dataset of 1020 images | AlexNet,VGG-16,VGG-19,SqueezeNet,GoogLeNet MobileNet-V2, ResNet-18,ResNet-50, ResNet-101,Xception | ResNet-101 and Xception |
| Singh et al. [24] | Public dataset of 1419 images | Modified XceptionNet | Modified XceptionNet |
| Panwar et al. [25] | Publicly available 337 images | VGG-16 inspired nCOVnet | VGG-16 inspired nCOVnet |
| Wang et al. [26] | Publicly available 3545 images | ResNet50 + FPN inspired model | ResNet50 + FPN inspired model |
| Abraham et al. [27] | 531 COVID-19 images; total 1100 images | 25pretrained networks (10 Basic and 15 Hybrid) | SqueezeNet + DarkNet-53 + MobileNetV2 + Xception + ShuffleNet |
| Toraman et al. [28] | Approximately public 731 images | Convolutional capsule network architecture | Convolutional capsule network architecture |
| Ozturk et al.[29] | Approximately public 627 images | Darknet inspired model | Darknet inspired model |
| Xu et al.[19] | Private 618 images | ResNet-18-based classification model | ResNet-18-based classification model |
| Khan et al. [30] | 1200 images of two public datasets | CNN model based on Xception architecture pre-trained on ImageNet dataset | CNN model based on Xception architecture pre-trained on ImageNet dataset |
| Ucar et al. [31] | 2800 images (consisting 45 images of COVID-19) from two public dataset | Deep Bayes-SqueezeNet inspired model | Deep Bayes-SqueezeNet inspired model |
| Nour et al. [2] | 2905 images (consisting 219 images of COVID-19) | CNN-Machine Learning-Bayesian Optimization-based Model | CNN-Machine Learning-Bayesian Optimization-based Model |
| Brunese et al. [32] | 6523 images (consisting 250 images of COVID-19) | VGG Inspired model | VGG Inspired model |
| Panwar et al. [33] | Private dataset of 526 images and Public dataset of 1300 images | Applying Grad-CAM technique in VGG-19 inspired model | Applying Grad-CAM technique in VGG-19 inspired model |
| Goel et al. [34] | 800 COVID-19 images; total 2600 images | Self-created CNN-based OptCoNet model | Self-created CNN-based OptCoNet model |
| Jain et al. [35] | 490 COVID-19 images; total 6432 images |
Inception V3, Xception and ResNetXt |
Xception |
| Abbas et al. [36] | 105 COVID-19 images; total 200 images | Self-composed a Deep CNN-based DeTraC model; (Uses AlexNet,VGG-19,GoogleNet, Resnet, SqueezeNet for the transfer learning stage in DeTraC) | VGG-19 in DeTraC |
| Zebin et al. [37] | 202 COVID-19 images; total 802 images | VGG-16,Resnet50 and EfficientNetB0 | EfficientNetB0 |
| Punn et al. [38] | 108 COVID-19 images; total 1200 images |
ResNet,Inception-v3,InceptionResNet-v2,DenseNet169, and NASNetLarge |
NASNetLarge |