[Retracted] COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Figure 9

Epochs versus loss graph for all CNN models.

(a) A loss results in a bad prediction. Both the training loss and validation loss curves decrease to zero. So the VGG16 model is more perfectly predicted than other models

(b) In the DenseNet121 architecture, the training curve is stable with a lower rate, while the validation curve simultaneously increases and decreases

(c) The training and validation curves both decrease in the MobileNet architecture. The loss is at zero, so the model predicted it correctly

(d) In the Xception architecture, the training curve is stable with a lower rate, and the validation curve increases and decreases simultaneously, until finally the loss is decreased to zero

(e) In the NASNet architecture, the training curve is stable, with a lower rate, and the validation curve increases and decreases simultaneously. Finally, the loss is decreased to zero, and the model is predicted correctly

(f) In the EfficientNet architecture, the training curve is stable with a lower rate, and the validation curve increases and decreases simultaneously. Finally, the model predicts the correct outcome