TABLE 4.

Comparison of the proposed automatic COVID‐19 diagnostic model with other deep learning‐based state‐of‐the‐art models

Study	Year	Type of radiological images	Methods	Class labels	Overall accuracy (%)
Amyar et al 20	2020	Chest CT	Deep learning‐based multitask model	COVID‐19 Non‐COVID	86
Ying et al 38	2020	Chest CT	DRE‐Net	COVID‐19 Pneumonia (bacterial)	86
Xu et al 42	2020	Chest CT	ResNet with location attention	COVID‐19 Influenza viral pneumonia Healthy	86.7
Ozturk et al 41	2020	Chest X‐ray	DarkCovidNet‐19	COVID‐19 NO‐finding Pneumonia (non‐COVID)	87.02
Li and Zhu 43	2020	Chest X‐ray	DenseNet	Pneumonia Normal COVID‐19	88.9
Hemdan et al 39	2020	Chest X‐ray	COVIDX‐Net	COVID‐positive COVID‐negative	90
Zheng et al 23	2020	Chest CT	3D deep CNN	COVID‐positive (data augmentation) COVID‐negative	90.8
Wang et al 44	2020	Chest X‐ray	Tailored deep CNN	Normal Pneumonia COVID‐19	92.6
Sethy and Behera 40	2020	Chest X‐ray	ResNet50 + SVM	COVID‐positive COVID‐negative	95.38
Ucar et al 21	2020	Chest X‐ray	Squeeze‐Net with Bayes optimization	Normal Pneumonia (bacterial) COVID‐19 (data augmentation)	98.26
Proposed model	2020	Chest CT	CNN	COVID‐positive COVID‐negative	93.26

Abbreviations: CNN, convolutional neural network; DRE‐Net, detail relation extraction neural network; SVM, support vector machine.