[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%

Bold entries represent the best value.