## 4 Experiments
### Cervical Cancer Classification
#### 4.1.3 Results  
The \(T_{sim}\) training for cervical cancer classification used the Intel and MobileODT dataset. Because it was important to see the visible transformation zone to identify cervical cancer, we pre-trained it to classify the transformation zone. As shown in Table 3, cervical cancer classification using transfer learning with a similar image showed better performance than the baseline. In cervical cancer classification, there are many parts outside the transformation zone (the area where cervical cells are most likely to become cancerous) that are unnecessary for diagnosing cancer.  
The \(T_{ssl}\) model learns the global context of the image to separate the unnecessary parts and then learn the local features needed for image classification. After training with the patch self-supervised learning method was completed, cervical cancer was classified using the transferred model. The \(T_{ssl}\) row in Table 3 shows that cervical cancer classification using transfer learning with the self-supervised learning model had higher accuracy and sensitivity than the baseline.  
\(T_{roi}\) learning extracts the cervical cancer RoI. As shown in the cervix image in Figure 3, when the cervix is viewed from the bottom, it looks like a circular shape with a center, which is a hole in the lower part of the cervix. When there is a blob-like region around the center, the transformation zone covers most of the abnormal cervical cancer tissue. Therefore, because it is important to see the circular part when determining cervical cancer, the baseline model-based U-Net was trained to segment the circular RoI. Then, the U-Net encoder was extracted, and cervical cancer classification was performed using the encoder. As shown in Table 3, the most improved performance was obtained when the model with RoI transfer learning was used.  
Furthermore, in the same way that clinicians detect and diagnose transformation zones simultaneously, multi-task learning that performed segmentation and classification only in the circular area demonstrated better accuracy and specificity than the baseline model.  
Finally, the transfer learning approaches with greater accuracy than the baseline model were ensembled and evaluated on the test set. The ensemble had the highest sensitivity. Because the \(T_{ssl}\) and \(T_{roi}\) had lower specificity than the baseline model, the ensemble model had low specificity. However, the sensitivity of the ensemble model was the highest of all the approaches. As sensitivity is an important metric in medical diagnosis, this is a significant result. The results showed that classifying an image that had unnecessary parts (e.g., a medical device or photograph date) hampered training features, so transfer learning with RoIs was an effective approach.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Experiment** & \multicolumn{3}{c}{**Metrics**} \\  & **Acc (\%)** & **Specificity (\%)** & **Sensitivity (\%)** \\ \hline Baseline & 67.2 & 74.3 & 57.3 \\ \(T_{sim}\) & 74.4 & 77.3 & 69.7 \\ \(T_{ssl}\) & 69.2 & 58.8 & 85.5 \\ \(T_{roi}\) & **77.9** & 71.4 & 88.2 \\ \(T_{multi}\) & 74.9 & **85.7** & 57.9 \\ \hline Ensemble & 77.4 & 68.9 & **90.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transfer learning results for cervical cancer classification.