Table 2. Optimal hyperparameters and classification results for CADx by DCNN with and without transfer learning.
Type | L | E | R | V | F | D | Validation Accuracy (%) | Validation Loss |
---|---|---|---|---|---|---|---|---|
DCNN with TF | ||||||||
56 | 20 | 0.00002 | 4 | 384 | 0.6 | 60.7 | 0.822 | |
112 | 20 | 0.00002 | 11 | 384 | 0.4 | 64.7 | 0.783 | |
224 | 20 | 0.00002 | 11 | 384 | 0.4 | 68.0 | 0.774 | |
DCNN without TF | ||||||||
56 | 30 | 0.00007 | 0 | 384 | 0.6 | 60.2 | 0.843 | |
112 | 25 | 0.0001 | 0 | 384 | 0.4 | 62.4 | 0.824 | |
224 | 15 | 0.0001 | 0 | 384 | 0.4 | 58.9 | 0.860 |
validation loss and validation accuracy were calculated 10 times with the same CADx hyperparameters, and their averaged values were shown. Abbreviations: CADx, computer-aided diagnosis; DCNN, deep convolutional neural network; TF, transfer learning.