Table 4.

Quantitative comparison of nodule detection methods. Accuracy, sensitivity for false positive per image (FPPI), and inference time are reported for comparison. For [33] training time is reported. SGD is the abbreviation for stochastic gradient descent, ReLu for rectified linear unit, lr for learning rate, and AF for activation function

Method	Optimizer	AF	LR scheduling	Images size	Pre-processing step	Dataset	Technique	Accuracy	Sensitivity	Time(s)
ANN [100]	–	–	–	32 $\times$ 32	Background removal for nodule enhancement and contrast enhancement	Private	CNN with fuzzy training, circular background subtraction technique	–	–	15
ANN [33]	Gradient-descent	–	lr = 0.1	128 $\times$ 128, 512 $\times$ 512	Lung field segmentation using deformable models (snakes)	JSRT, Private	Neural network, LoG, gabor kernel	95.7-98.0(4-10)	60.0-75.0(4-10)	500
CAD [145]	–	–	–	256 $\times$ 256	Deformable model (ASM) is used for lung field segmentation and local normalization is performed to achieve the global contrast equalization	JSRT	ASM, local normalization (LN) filtering, Lindeberg detector, Gaussian filter bank	51.0(2), 67.0(4)	–	–
CAD [25]	–	–	–	2048 $\times$ 2048	Lung segmentation using multi-segment ASM, gray-level morphological operators for enhancing nodules and rib suppression	JSRT, Private	ASM, watershed algorithm, leave-one out cross-validation, SVM with a Gaussian kernel	–	77.1(2), 83.3(5)	70
ANN [24]	–	–	–	2048 $\times$ 2048	Lung segmentation using multi-segment ASM, gray-level morphological operators for enhancing nodules and rib suppression using MTANNs	JSRT, Private	VDE, MTANN, morphological filtering, support vector classifier	–	85.0(5)	115
DL [93]	SGD	ReLu	–	229 $\times$ 229	Unsharp mask sharpening technique	JSRT	Unsharp mask sharpening, E-CNN, five-fold cross-validation	–	84.0(2), 94.0(5)	–
DL [95]	SGD	ReLu	lr = 0.001 which drops 0.00001 after every iteration	224 $\times$ 224	Random rotation and mirroring, image enhancement with gray-level stretching and histogram matching, lung field segmentation and rib suppression using ASM and PCA, respectively	JSRT, Shenzhen	ASM, PCA, dense blocks, fivefold cross-validation	99.0(0.2)	–	–
DL [23]	SGD	ReLu, Softmax	lr = 0.001	229 $\times$ 229	Horizontal inversion, angle rotation and flipping, lung field segmentation using multi-segment ASM	JSRT	ASM, watershed algorithm, GoogLeNet	–	91.4(2), 97.1(5)	–