Table 11.
Comparison of Corona-Nidaan with other previously published approaches developed using chest X-ray images
| Study | Number of Samples | Methods | Accuracy | Params (in million) | Remarks |
|---|---|---|---|---|---|
| Wang and Lin et al. [35] | 183 COVID-19 8,066 Normal 5,538 Pneumonia | COVID-Net b | 92.6% | 117.4 | Model suffers from false-negative results for COVID-19 cases and consists of more number of trainable parameters. |
| Hemdan et al. [12] | 25 COVID-19 25 Normal | COVIDX-Net(VGG-19) a | 90% | 20.55 | 93% of accuracy found on our dataset. |
| Ozturk et al. [22] | 125 COVID-19 500 Normal 500 Pneumonia | DarkCovidNet a | 87.02% | 1.16 | Model suffers from false-positive and false-negative results. Under-sampling technique loses important details of pneumonia and normal classes. 71% of accuracy found on our dataset. |
| Mangal et al. [19] | 115 COVID-19 1,341 Normal 3,867 Pneumonia | CovidAID c | 90.5% | – | Model suffers from false-negative cases in case of normal class. |
| Apostolopoulos et al. [1] | 224 COVID-19 504 Normal 700 Pneumonia | VGG-19 a | 93.48% | 20.55 | 93% accuracy found on our dataset. |
| Basu et al. [2] | 225 COVID-19 350 Normal 322 Pneumonia 50 Other-disease | 12 layer CNN c | 95.3% | – | The proposed model trained on very limited Normal and Pneumonia samples. |
| Oh et al. [21] | 180 COVID-19 191 Normal 54 Bacterial Pneumonia 57 Tuberculosis 20 Viral Pneumonia | FC-DenseNet103 + ResNet-18 b | 91.9% | 11.6 | Model is trained with very limited number of samples, suffers from false positive and negative results. More number of trainable parameters. |
| Khan et al. [17] | 284 COVID-19 310 Normal 330 Bacterial Pneumonia 327 Viral Pneumonia | CoroNet (Xception) c | 89.6% | 33 | More number of trainable parameters, trained on limited number of training samples, Model mis-classifies many Pneumonia cases as Normal. |
| Perumal et al. [23] | 205 COVID-19(X-ray) 1,349 Normal 2,538 Bacterial Pneumonia 202 COVID-19(CT) 1,345 Viral pneumonia | VGG-16 c | 93% | – | Model sometimes mis-classifies COVID-19, viral pneumonia, and Normal cases. More number of trainable parameters as VGG-16 used. Manual pre-processing and feature generation used. |
| ours | 245 COVID-19 8,066 Normal 5,551 Pneumonia | Corona-Nidaan | 95% | 4.02 | End to end learning approach, low mis-classification rates, lightweight, and less number of trainable parameters. |
a Implemented, trained and tested against our dataset
b Tested on the same samples as we did
c Reported accuracy by the author in their work