ANN [100] |
– |
– |
– |
32 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 |
128 128, 512 512 |
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] |
– |
– |
– |
256 256 |
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 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 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 |
– |
229 229 |
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 |
224 224 |
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 |
229 229 |
Horizontal inversion, angle rotation and flipping, lung field segmentation using multi-segment ASM |
JSRT |
ASM, watershed algorithm, GoogLeNet |
– |
91.4(2), 97.1(5) |
– |