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

Figure 8

Confusion matrix of CNN models.

(a) VGG16 outperforms other models, predicting 342 correctly identified COVID images and 333 correctly identified non-COVID images out of 691 samples

(b) DenseNet121 has more layers, which reduces parameter efficiency and makes it more prone to overfitting. This model predicts 353 correctly identified COVID images and 321 correctly identified non-COVID images out of 691 samples

(c) Even though MobileNet is faster in performance, it gives less accuracy compared to the VGG16 model. The MobileNet model predicts 334 correctly identified COVID images and 332 correctly identified non-COVID images

(d) The number of parameters trained is larger compared to other models. Xception model predicts 341 correctly identified COVID images and 298 correctly identified non-COVID images

(e) The NASNet architecture predicts 297 correct COVID images and 322 correct non-COVID images

(f) EfficientNet predicts 144 correctly identified COVID images and 292 correctly identified non-COVID images with lower accuracy