Research Article
[Retracted] Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
Table 12
Comparative analysis of classification metric.
| Method/References | Description | Classification model/tool | Metric |
| IWOA | DWT-PCA texture features from the IWOA-based segmented image | Random forest | Accuracy: 97.49% | WOA | DWT-PCA texture features from the WOA-based segmented image | Random forest | Accuracy: 93.26% | SSA | DWT-PCA texture features from the SSA-based segmented image | Random forest | Accuracy: 94.12% | SCA | DWT-PCA texture features from SCA-based segmented image | Random forest | Accuracy: 90% | Reference [6] | Fine-tuned DL architectures to detect COVID-19 from chest X-ray images | DenseNet121 | Accuracy: 97% | Xception | Accuracy: 96% | MobileNetv2 | Accuracy: 95% | ResNet50v2 | Accuracy: 94% | VGG19 | Accuracy: 92% | Inceptionv3 | Accuracy: 90% | Reference [8] | COVID-19 lung CT image segmentation to assess the diagnosis of COVID-19 patients | SegNet | Accuracy: 95% | U-NET | Accuracy: 91% | Reference [9] | Identification of COVID-19 images from chest X-rays using DL | Cognex’s Vision Pro DL software | F-Score: 94% |
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Bold entries represent the best value.
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