## 4 Experiments
### Skin Lesion Classification
#### 4.2.3 Results  
The \(T_{sim}\) experiment for skin lesion classification used the ISIC 2017 dataset. Table 5 shows the performance when the \(T_{sim}\) approach was applied to skin lesion classification using a transferred model with similar images. Because the ISIC 2017 dataset has fewer classes than the HAM10000 dataset, training for seven types on the HAM10000 datasets using the transferred weight from \(T_{sim}\) showed slightly worse accuracy and precision but better sensitivity.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Melanoma** &
\begin{tabular}{c} **Seborrheic** \\ **Keratosis** \\ \end{tabular} & **Nevus** & **Total** \\ \hline Training set & 374 & 254 & 1372 & 2000 \\ Validation set & 30 & 42 & 78 & 150 \\ Test set & 117 & 90 & 393 & 600 \\ \hline Total & 521 & 386 & 1843 & 2750 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of images in the ISIC dataset according to skin lesion type.  
Figure 8: Skin lesion images of each class (from left to right, vasc, nv, mel, df, bkl, bcc, and akiec) in the HAM10000 dataset.  
Because labeling lesion diagnosis is expensive, there are many unlabeled images. Therefore, transfer learning with patch self-supervised learning was used as an approach for using images without labels. Unlike the cervical images, the skin lesion images were taken with a dermatoscope, and most skin patches look similar to each other except for the area with the lesion. When the \(T_{ssl}\) approach, which predicts the arrangement of patches, was applied, the accuracy was lower than the baseline accuracy, but the precision was 2.2% better, as shown in Table 5.  
Because the images of the HAM10000 dataset were photographed with a dermatoscope, the areas where skin lesions exist were clearly separated, as shown in Figure 8. However, the model needed to learn to separate the lesion area from normal skin. Therefore, it was trained to segment lesions according to the lesion shape. After a decoder was attached to the baseline model DenseNet121 using an up-sampling layer and the model was transformed into a U-Net model, the U-Net model was trained to segment the RoI in accordance with the lesion shape. Then, the U-Net encoder was extracted, and the skin lesions were classified using the encoder. As shown in Table 5, the accuracy and precision were improved by 1.8% and 5.8%, respectively, compared to the baseline.  
When \(T_{multi}\) learning was used to train both skin lesion segmentation and classification simultaneously, as inspired by clinicians' diagnoses, the performance was worse than when training a single task. Because skin images taken with a magnifying glass were clearly distinguished by the average pixel value of the skin lesions and other pixels, multi-task learning appeared to cause confusion. In addition, according to [94], multi-task learning has a destructive interference relationship in which the performance of one task is improved while the performance of another task is degraded. The author stated that the division of information between tasks is a fine line to walk.  
However, when ensemble learning was used with different initial weights using \(T_{roi}\), the accuracy was higher than the baseline accuracy, showing the highest accuracy, precision, and sensitivity. Therefore, for the dataset photographed with a dermatoscope or magnifying glass or containing images that clearly distinguish between the normal area and the RoI (that is, between healthy skin and the lesion), as shown in Figure 8, transfer learning using the trained weights to segment the RoI based on the shape performed best.