Table 5.

Discussion and comparison with existing studies

Author	Images considered		Architecture used	Visualization method	Validation method	COVID-19 detection Results (%)		Remarks/Limitations
	Healthy	COVID-19
Ozturk et al. [20]	1000	250	DarkNet-17	Grad-CAM	5-fold CV	Sensitivity:	90.65	Imprecise localization of areas on the chest region
						AUC-ROC:	–
Brunese et al. [8]	3520	250	VGG-16	Grad-CAM	CV	Sensitivity:	87	Proposed to investigate if formal verification techniques can be helpful to obtain better results
						AUC-ROC:	–
Mahmud et al. [13]	305	305	Stacked Multi-resolution CovXNet	Grad-CAM	5-fold CV	Sensitivity:	97.8	Scattering in gradient based localizations out of the region of interest
						AUC-ROC:	96.9
Rajaraman et al. [36]	1583	314	Wide residual network and pretrained models	Grad-CAM	Random Split	Sensitivity:	–	Very small collection of COVID-19 data to select augmented training images, Imbalanced dataset and Imprecise localization of areas on the chest region belonging to COVID-19
						AUC-ROC:	–
Das et al. [17]	D1:1583 D2: 80	162 162	Truncated Inception Net	Activation map	10-fold CV	Sensitivity:	95	Maximum values are reported for imbalanced dataset. Poor localization of areas of COVID-19
						AUC-ROC:	99
Proposed Work	150	151	CNN - 5	Occlusion sensitivity	5-fold CV	Sensitivity:	97.35	Simplified, efficient CNN network for limited dataset Perturbation based visualization method for precise localization
						AUC-ROC:	99.4