. 2021 Feb 20;67:102518. doi: 10.1016/j.bspc.2021.102518

Table 3.

Comparison of the proposed method with State-of-the-Art Methods.

Author and Year	Deep Learning Module Used	Type of Diseases Involved	Dataset and type of image	Results
				Metric Name	Metric Value
Zheng C et al. (Jan 2020) [16]	Deep CNN	COVID-19	Local Hospitals 630 CT Images	Accuracy	0.9
Xiaowei Xu et al. (Feb 2020) [8]	3D-CNN	COVID-19	CT images (Hospitals in China) 618 samples	Accuracy	0.87
		Influenza-A viral pneumonia
		Healthy People
Ali Naren et al. (March 2020) [13]	ResNet50	COVID-19	GitHub and Kaggle repository 100 chest X-ray Images	Accuracy	0.98
	Inception -ResNetV2				0.87
	InceptionV3				0.97
Gozes et al. (March 2020) [17]	ResNet-50 based 2-D CNN	Covid-19	56 CT Images from Local Hospitals	Sensitivity	0.98
				Specificity	0.92
				AUC	0.99
Barstuga et al. (March 2020) [18]	Feature Extraction –GLCM, LDP, GLRLM, GLSZM, DWT	COVID-19	Local Hospitals 150 CT abdominal Images	Accuracy	0.99
Barstuga et al. (March 2020) [18]	Classifier- SVM	COVID-19	Local Hospitals 150 CT abdominal Images	Accuracy	0.99
Proposed Method	U-Net	COVID-19	1000 Chest CT Images GitHub Repositories & SIRM	Sensitivity	0.92
				Specificity	0.93
				Accuracy	0.94
				Precision	0.95