# A Reinforcement Learning Algorithm for Automated Detection of Skin Lesions
## 4 Results and Discussion
### Experimental Setup
#### 4.1.3 Evaluation and Comparison on the ISIC 2017 Dataset, HAM10000, and the PH2 Dataset
The participants in the ISIC-2017 segmentation challenge segmented the borders of the lesion regardless of the lesion form. The value of the Jaccard index, _Sen_, _Spe_, _Dice index_, and _Acc_ are 0.84, 0.95, 0.985, 0.957, and 0.9539. The percentage of positive individual pixel values that are correctly identified and the negative values that are correctly identified is higher than the other methods. The majority of the lesion regions have a darker background and a lighter boundary. As a result, the segmentation frameworks find the darker center but lacks the bright border regions. Our method outperforms all other segmentation algorithms for each skin lesion and can pick up the bright border regions. Figure 10: The visual images taken from ISIC 2018 image database model (present without the black frame). (**a**) The original image, (**b**) the given binary mark (ground truth), (**c**) the prediction effects of the method (Att U-Net + GN). (**d**) The results of our RL algorithm. As can be seen, our segmentation masks corners are sharp and clear. Figure 11: **(a)** Original input image, (**b**) ground truth images, (**c**) segmentation results of simple U-Net, (**d**) segmentation results of SE block on basic U-Net, (**e**) segmentation results of the BCDU network (with 1 dense unit), (**f**) segmentation results of the U-Net network (with all 64 filters), and (**g**) segmentation results of Att U-Net + GN + TL. (**h**) Results of our RL algorithm. As can be seen from the image, the results of the masks are clear. Our algorithm is able to segment the lesions with a high _Acc._ The other statistical measures are also higher than the other methods. Figure 12: **(a)**The statistical measures used by our method such as _Acc, Dice,_ Jaccard index, _Sen_, and _Spe_ are plotted for the PH2 and the ISIC 2017 skin segmentation dataset. The blue line denotes the performance metrics plotted for the PH2 dataset and the red line shows the metrics for ISIC 2017 skin segmentation dataset. In (**b**), the statistical measures of our method denoted as Dice, JSI, MCC, and the overall of these statistical measures are plotted for the three categories of skin lesions: naevus, melanoma, and seborrheic keratosis. While the other blue line and the red line denotes the metrics for the PH2 and the ISIC 2017 dataset, the highlighted points indicate the metric values such as _Sen, Spe_, and _Acc_ for each of the categories on both the datasets. (**c**) Shows the action bundle effect. The various values of K are the different settings for the action bundle. We evaluate the effect of the value K on the dice values. We infer from the results that our segmentation results improve when we use both the modified ERM and action bundle. The qualitative results show our model's segmentation mask performing better than the other models. The statistical measures of our RL algorithm are compared to the results of the standard algorithms, such as U-Net [36], SegNet [38], FrCN [42], etc. The _Acc_, dice values are 95.39% and 95.7%, ensuring that our algorithm results are superior to the other \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Statistical Measure** & **Basic Model** & **Modified ERM** & **With Action** & **With Both** & **K = 1** & **K = 3** & **K = 5** & **K = 7** \\ \hline Dice Index & 93.00 & 93.98 & 94.0 & 95.7 & 93.0 & 93.98 & 95.79 & 94.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Dice index values taken for the ablation experiments. The various values of K are the different settings for the action bundle.