Table 2.

Different nodule detection and characterization system using CNN-based deep learning approach in lung CT images

Sl. No.	First author with publication year	Method	Purpose	Performance
1.	Paul et al. (2018) [37]	Pre-trained vgg-s network with merge CNN	Nodule characterization	Accuracy = 76.79%, ROC = 0.87
2.	Liu et al. (2018) [157]	Multi-view multi-scale CNN	Nodule categorization	Overall accuracy = 92.3% (LIDC dataset) and 90.3% (ELCAP dataset)
3.	Yuan et al. (2017) [155]	Pre-trained vgg network followed by CNN-based features fused with handcraft-based features (multi-view multi-scale CNN)	Nodule categorization	Overall accuracy = 93.1% (LIDC dataset) and 93.9% (ELCAP dataset)
4.	Xie et al. (2017) [156]	Applied CNN at decision level	Nodule characterization	AUC of 96.65%, 94.45%, and 81.24%
5.	Silva et al. (2017) [154]	Genetic algorithm-based CNN	Nodule characterization	Sensitivity = 94.66%, specificity = 95.14%, accuracy of 94.78%, and area under the ROC curve of 0.949.
6.	Wang et al. (2017) [153]	Data-driven-based machine learning model using central focused convolutional neural network (CF-CNN)	Nodule segmentation	Dice score = 82.15% (LIDC dataset) and 80.02% (GDGH dataset)
7.	Dou et al. (2016) [152]	Three 3-D CNN applied with fusion technique	Nodule detection	Sensitivity = 94.4% and sensitivity = 92.2% at 8FPs/scan
8.	Shen et al. (2016) [149]	Multi-crop convolutional neural network (MC-CNN)	Nodule characterization	Accuracy = 87.14%, AUC = 0.93, sensitivity = 77%, specificity = 93%
9.	Setio et al. (2016) [150]	Multiple streams of 2-D ConvNets (multi-view architecture)	Nodule detection	Sensitivity = 85.4% with 1 FP/scan and 90.1% with 4 FPs/scan
10.	Ciompi et al. (2015) [151]	2-D convolution-based architecture named as “OverFeat”	Peri-fissural nodules (PFNs) detection	Area under curve (AUC) = 0.868