Table 1.

Comparison of various techniques used for COVID-19 detection.

Ref	Technique	Data Type	Data Source	Accuracy
[12]	Transfer Deep Learning for automatically predicting COVID-19	X-Ray	Kaggle and GitHub	98%
[13]	Automated Technique for Detecting and Classifying Pneumonia-based using Deep Learning	CT and X-Ray	X-Ray, CT Dataset publicly available on the internet	96%
[14]	Deep Learning for Screening COVID-19 pneumonia	CT	Hospital of Zhejiang, China	86.7%
[15]	Deep CNN	X-Ray	X-ray images of a public dataset	VGG19, DenseNet models: f-scores = 0.89 normal & Coronavirus-19 = 0.91
[7]	Automated Deep Convolutional Neural Network	X-Ray	50 Coronavirus patients (GitHub) 50 normal X-ray (Kaggle)	98%
[16]	Support Vector Machine	CT	Total = 150 CT images Coronavirus = 53	Classification accuracy result obtained from GLSZM = 99.68%
[17]	Support Vector Machine based on deep learning approach (Deep Features)	X-Ray	Coronavirus cases = 25 Normal cases = 25 (GitHub, Kaggle)	Accuracy: SVM + ResNet50 (FPR = 95.52%, 1 score = 95.52%, MCC = 91.41% and Kappa = 90.76%)