## 4 Experiments and Discussion  
In order to evaluate the classification performance of the softmax classifier-based model, the SVM classifier-based model, and the proposed synthetic model, experiments are first carried out on the breast cancer dataset. In order to have less biased or less optimistic estimate of the models' performance, four-fold cross-validation setup is used. To simply ensure the balance of each class, 56,000 IDC images and 56,000 non-IDC images are used. The selected images are equally split into four groups with the same number of images for each group. For fold number k, the kth group is used for validation and the other three groups combined are used for training. The process is repeated four times. During the splitting of the dataset, stratified sampling is used in order to eliminate sampling bias, the number of images for each class of each split group are kept the same, i.e., 14,000 IDC images and 14,000 non-IDC images in each group. Same folds are used for all three models. It should be noted that the patient-wise data portioning is not currently considered in this paper, which will be improved in the future work. The softmax classifier-based model is first trained using the above cross-validation setup. The images are first resized to 227 \(\times\) 227 pixels to fit the input size of the transferred AlexNet. The transferred layers are assigned a minimal learning rate to make sure the weights in these layers are frozen, and the features learned from the ImageNet database can be successfully transferred to the target dataset. For each fold of cross-validation, the training is carried out for 10 epochs using the Adam solver. The training set is divided into  
Figure 6: The proposed transfer learning model for histopathology image classification. mini-batches with a size of 100. For each iteration, a mini-batch is used for training, and a different mini-batch is used in the next iteration. The training is conducted throughout the whole training set in 840 iterations or one epoch. The total number of iterations is 8400. The training progress of the four folds of cross-validation is shown in Figure 7, including training accuracy and training loss. The classification accuracy on the validation set for each fold are shown in Table 1, which ranges from 0.6887 to 0.8579, and the average cross-validation accuracy is 0.7806. The confusion matrix of the softmax-based model is shown in Figure 8.  
For the cross-validation of the proposed synthetic model, after obtaining the softmax-based model trained on the breast cancer dataset in the nth fold, the newly trained FC layer with the transferred AlexNet in the softmax-based model is used for feature extraction in the nth fold cross-validation of the proposed model. The dataset configuration is exactly the same as in the previous experiments. For the SVM classifier in the proposed approach, linear kernel is used due to its best accuracy on the target dataset compared with other kernels, such as the Gaussian kernel and high-order polynomial kernel. The cross-validation accuracy is also shown in Table 1 for comparison, which ranges from 0.6916 to 0.8558, and the average cross-validation accuracy is 0.7840. The corresponding confusion matrix is shown in Figure 8.  
Cross-validation is performed in SVM-based model, linear kernel is used as well. Attached to the frozen transferred AlexNet layers, the SVM classifier is trained on the breast cancer dataset, and the average classification accuracy on the validation set for the four-fold is 0.6877. The confusion matrix is shown in Figure 8.  
By comparing the three models for binary breast cancer histopathology image classification, the SVM-based model is less computationally intensive. However, the average cross-validation accuracy of this model is the worst among the three models, which is 11.9% lower than that of the softmax-based model and 12.2% lower than the proposed model.