Table 2. Review of lung nodule candidate detection algorithms.

Year	Authors	Method	Accuracy	False positive rate
2008	Ozekes et al. (29)	3D template matching	90.00%	13.38
2009	Ye et al. (26)	Filtering-based	90.20%	8.2
2011	Pu et al. (23)	Shape-based	70.00%	N/A
2011	Kubota et al. (25)	Convexity model and morphological approach	69.00%	N/A
2012	Cascio et al. (30)	Stable 3-D mass spring models	97.00%	6.1
2012	Soltaninejad et al. (31)	Active contour and k-nearest neighbors algorithm	90.00%	5.63
2013	Choi et al. (32)	Entropy analysis	99.00%	2.27
2014	Jo et al. (24)	Template matching	91.00%	N/A
2016	Akram et al. (22)	Multiple grey-level thresholding	97.52%	N/A
2016	Gonçalves et al. (33)	Hessian matrix–based method	N/A	N/A
2018	Naqi et al. (27)	Polygonal approximation	97.70%	N/A
2019	Huidrom et al. (28)	Neuro-evolutionary scheme	93.20%	N/A