TABLE IV. Pseudocode of Our 7L-CNN-CD Model.

Input: Original Image Set
Ground Truth: obtained from two junior and one senior radiologists. See Eq. (1.a)
Phase I: Preprocessing
Grayscale . See Eq. (3)
Histogram Stretching . See Eq. (5.a)
Image Crop . See Eq. (6.a)
Downsampling . See Eq. (7)
Phase II: 10 runs of 10-fold cross validation
for % is run index
Randomly split preprocessed set into 10 folds
,
for :10 % is trial index
Step II.A: Training & Test Set
is chosen as the t-th fold.
Training Set. B is chosen as the other folds.
Enhanced Training Set.
DA , see equation (27).
Step II.B: Create Initial CNN model
Create an initial deep network via 7L-CNN model;
Use SP to replace all pooling layers in 7L-CNN model. See equation (34).
Step II.C Trained 7L-CNN-CD model
Train 7L-CNN network using and ground truth
Trained model
;
Step II.D: Confusion Matrix Performance
Test prediction
Test performance. is obtained by comparing test prediction and ground truth.
.
end
Summarize all 10 trials and get , see Eq. (38).
Calculate , see Eqs. (39.a)–(42)
end
Output mean and SD of . see Eq. (43.a)