Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains

Table 14

The comparison of the proposed approach along with the state-of-the-art methods on the MRI dataset (unit: %).

State of the art	Weighted average recognition rates	Standard deviation
Logistic regression	74.0	±3.1
Support vector machine	78.0	±1.8
Random forest	77.0	±2.4
k-nearest neighbor	81.0	±1.1
Artificial neural network	79.0	±3.7
Naïve Bayes	71.0	±5.2
Decision tree	77.0	±2.6
Passive aggressive classifier	70.0	±4.6
Multilayer perceptron	47.0	±6.1
Extra tree classifier	79.0	±4.4
Proposed model	86.0	±3.5