## 4 Discussion  
The first set of analyses examined the robustness of the TLSE method on different CNN models by comparing the classification results. As shown in the Table 2, all three models get comparable results. It is evident from the table that the TLSE method works well with different CNN architectures on the Herlev dataset. The classification rate of Inception-ResNet model is higher than the other two models. The improvement of accuracy on the Inception-ResNet model is due to the availability of deeper CNN architecture, which helps the model to learn patterns that have more diversity and better representation power. Table 2 also presents the transfer learning method with the same CNN models. By comparing the results with the TLSE method. There is a significant difference between these two methods, which is that the TLSE based method achieves higher accuracies. Turning now to the experimental evidence on proving that the addition of snapshot ensemble in TLSE has a positive influence on the fine-grained cervical cells classification task. Furthermore, these results also provide important insights into the robustness of the proposed TLSE method, as Table 2 illustrates that the increments of accuracies are gained from all three models. The most striking result to merge from the data is that the TLSE method based on the Inception-ResNet model yield an accuracy of 65.56% on the pap-smear Herlev dataset, outperforming the single transfer learning method using the same base model. The positive influence of the TLSE to the final classification rate: get ensemble result through a single optimization process, add diversity and generalization to the CNN model. The original snapshot ensemble is trained on a benchmark dataset cifar10, and several data augmentation techniques are adopted to increase the amount of data for providing sufficient information \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Overall Error** \\ \hline DeepPap [4] & -(didn’t report) & 1.6\% (5-fold validation) \\ Benchmark [6] & 61.1\% & 7.9\% (10-fold validation) \\ Gen-wknn [13] & -(didn’t report) & 3.9\% (5-fold validation) \\ Fine-grained CNN [32] & 64.8\% & 7.7\% (5-fold validation) \\ TLSE & 65.56\% & 3.6\% (5-fold validation) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed TLSE with other previous deep learning methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Architecture Refinement** & **Accuracy** \\ \hline With all the refinements & 65.56\% \\ Without dropout & 63.16\% \\ Without dropout and BN layer & 62.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The classification results of different architecture refinements. for training deep CNN models. Nevertheless, in the medical image application area, the well-labelled data are not enough for training CNN model from scratch, and the training of CNN models will fall is a high probability event because of the over-fitting problem. Fine-tuning the pre-trained CNN towards the target dataset can provide model with a strong ability of robustness and generalization. The pre-trained model with the fine-tuning phase can solve the problems of bad training approximation and pool generalization. The experiment results show that the TLSE method is effective and the established model offers a better accuracy and good capability of generalization. The results obtained from Table 3 reveals that the model architecture refinements based on Inception-ResNet also improve classification accuracy. These minor refinements have a positive impact on the final classification accuracy. As expected, the combination of these techniques achieves a better accuracy compared to the non-modification architecture model, these architecture refinements help in capturing the high-level structural information, yielding better discriminability of different kinds of cervical cells. Further analyses reveal that the modification of the model architecture is effective and offers the model a better capability of generalization.