## 4 Experiments and Discussion
Even though the SVM-based model and the proposed model both feature an identical SVM classifier, the feature extraction using the FC layer trained on the target dataset in the proposed model largely outperforms the feature extraction using only the ImageNet-based AlexNet in the SVM-based model. The proposed model also shows a 0.4% improvement of the average cross-validation accuracy over the softmax-based model after  
Figure 7: Cont. To verify the proposed approach, experiments are then conducted on the multiclass lung and colon cancer dataset. Similar to the previous experimental setup, four-fold cross validation is used. The dataset is split into four groups, stratification is used in splitting the dataset, each group contains 1250 images for each class. The ratio of number of images for training and number of images for validation in each fold is 3:1. Same folds are used for all three models. The softmax-based model is first trained using Adam solver for 10 epochs with a mini-batch size of 100 in each fold, and the number of iterations is 1870. The training progress of the four folds are shown in Figure 9. The confusion matrix is shown in Figure 10. The cross-validation accuracy is shown in Table 2, and is above 0.99 for all four folds, and an average accuracy 0.9929 is achieved. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Softmax-Based** & **SVM-Based** & **Proposed** \\ \hline Fold 1 & 0.6887 & 0.5311 & 0.6916 \\ Fold 2 & 0.8579 & 0.7525 & 0.8558 \\ Fold 3 & 0.8036 & 0.7848 & 0.8150 \\ Fold 4 & 0.7720 & 0.6825 & 0.7734 \\ Average & 0.7806 & 0.6877 & 0.7840 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cross-validation accuracy of the three transfer learning models on breast cancer dataset. Figure 7: Training progress of the 4-fold cross-validation of the softmax classifier-based model on breast cancer dataset. Figure 8: Confusion matrix on breast cancer dataset. Figure 9: _Cont_. Figure 9: Training progress of the 4-fold cross-validation of the softmax classifier-based model on lung and colon cancer dataset. Figure 10: Confusion matrix on lung and colon cancer dataset. Then the proposed model is trained based on the already trained softmax-based model in the same fold. The SVM classifier is based on Error-Correcting Output Codes (ECOC) in order to classify multi classes other than binary classification. The validation accuracy for each fold is listed in Table 2, and also above 0.99 for all folds, the average accuracy is 0.994. The corresponding confusion matrix is shown in Figure 10.  
Similarly, ECOC is used in the SVM-based model, and the model is trained four folds, the cross-validation accuracy ranges from 0.9443 to 0.9642 with an average of 0.9560 is obtained. The confusion matrix is also shown in Figure 10.  
By comparing the three models for multiclass lung and colon cancer classification, the SVM-based model still has the lowest average cross-validation accuracy even though it is already above 0.95. The proposed model is again the winner among the three models, its average accuracy is 0.2% higher than that of the softmax-based model and 4.0% higher than that of the SVM-based model. No matter for binary histopathology image classification or for multiclass histopathology image classification, it is proved from the above experimental results that the proposed architecture with training twice on the target dataset enjoys more superior classification accuracy. The feature extraction empowered by the FC layer trained on the target dataset help enhance the classification performance of the proposed model.