A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

Table 7

The comparison of our pipeline with previously published CADs.

CAD system	CT image database	Number of cases	Nodule size(mm)	Sensitivity (%)	Average FPR
Dehmenski et al. [9]	Their own database	70	3–20	90.0	14.6
Suarez-Cuenca et al. [10]	Their own database	22	4–27	80.0	7.7
Opfer and Wiemeker [46]	LIDC database [47, 48, 50]	93	≥4	74.0	4
Rubin et al. [51]	Their own database	20	≥3	76	3
Sahiner et al. [49]	LIDC database [47, 48, 50]	48	3–36.4	79	4.9
Messay et al. [24]	LIDC database [47, 48, 50]	84	3–30	82.66	3
Suzuki et al. [52]	Their own database	101	8–20	80.3	16.1
Park et al. [53]	Their own database	38	Indefinite	80	–
Choi and Choi [23]	LIDC database [47, 48, 50]	32	3–30	94.1	5.45
Choi and Choi [44]	LIDC database [47, 48, 50]	58	3–30	95.28	2.27
Proposed method	Our database	47	3–35	97.42	4.54