# 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
Consequently, the computational complexity of the one-initialized Q-learning \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Naevus**} & \multicolumn{4}{c}{**Melanoma**} & \multicolumn{4}{c}{**Seborrheic**} & \multicolumn{4}{c}{**Overall**} \\  & _Dice_ & _JS1_ & _MCC_ & _Dice_ & _JS1_ & _MCC_ & _DICE_ & _JS1_ & _MCC_ & _DICE_ & _JS1_ & MCC \\ FCN-AlexNet [10] & 85.61 & 77.01 & 82.91 & 75.94 & 64.32 & 70.35 & 75.09 & 63.76 & 71.51 & 82.15 & 72.55 & 78.75 \\ FCN-32s [11] & 85.08 & 76.39 & 82.29 & 78.39 & 67.23 & 72.70 & 76.18 & 64.78 & 72.10 & 82.44 & 72.86 & 78.89 \\ FCN-16s [15] & 85.60 & 77.39 & 82.92 & 79.22 & 68.41 & 73.26 & 75.23 & 64.11 & 71.42 & 82.80 & 73.65 & 79.31 \\ FCN-8s [41] & 85.33 & 76.07 & 81.73 & 80.08 & 69.58 & 74.39 & 68.01 & 56.54 & 65.14 & 81.06 & 71.87 & 77.81 \\ DeepLabV3+ [51] & 88.29 & 81.09 & 85.90 & 80.86 & 71.30 & 76.01 & 77.05 & 67.55 & 74.62 & 85.16 & 77.15 & 82.28 \\ Mask R-CNN [48] & 88.83 & 80.91 & 85.38 & 80.28 & 70.69 & 74.95 & 80.48 & 70.74 & 76.31 & 85.85 & 77.39 & 81.99 \\ Ensemble-S [57] & 87.93 & 80.46 & 85.58 & 78.45 & 68.42 & 73.61 & 76.88 & 66.62 & 74.05 & 84.42 & 76.03 & 81.51 \\ [PERSON] et al. [16] & 88.87 & 81.69 & 85.93 & 83.05 & 74.01 & 77.98 & 81.71 & 72.50 & 77.68 & 86.66 & 78.82 & 83.14 \\ [PERSON] et al. [42] & 89.28 & 82.11 & 86.33 & 83.54 & 74.53 & 78.08 & 82.53 & 73.45 & 78.61 & 87.44 & 79.34 & 83.57 \\ SLSNet [76] & 86.59 & 78.76 & 79.80 & 92.12 & 79.25 & 79.53 & 86.12 & 74.52 & 77.12 & 88.27 & 77.54 & 78.81 \\ [PERSON] et al. [77] & 87.12 & 80.35 & 85.14 & 86.25 & 78.69 & 80.25 & 84.35 & 81.32 & 83.25 & 85.90 & 80.12 & 82.88 \\ [PERSON] et al. [78] & 88.12 & 79.14 & 80.12 & 89.12 & 77.24 & 80.37 & 86.37 & 83.40 & 81.42 & 87.87 & 79.90 & 80.63 \\ [PERSON] et al. [79] & 86.32 & 79.45 & 81.22 & 85.67 & 76.27 & 80.27 & 85.39 & 79.58 & 79.38 & 85.79 & 78.40 & 80.29 \\ Our RL algorithm & 93.00 & 89.57 & 90.78 & 95.79 & 91.93 & 87.11 & 95.00 & 93.23 & 92.74 & 94.59 & 91.57 & 90.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: For the ISIC 2017 test set, the segmentation results of our proposed RL algorithm and the current state-of-the-art segmentation methods. The statistical measures used are dice score, Jaccard similarity index (JS1), Mathews correlation coefficient (MCC), and the melanoma category seborrheic keratosis (SK). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Naevus**} & \multicolumn{4}{c}{**Melanoma**} & \multicolumn{4}{c}{**Seborrheic**} & \multicolumn{4}{c}{**Overall**} \\  & _Sen_ & _Spe_ & _Acc_ & _Sen_ & _Spe_ & _Acc_ & _Sen_ & _Spe_ & _Acc_ & _Sen_ & _Spe_ & _Acc_ \\ FCN-AlexNet [10] & 82.44 & 97.58 & 94.84 & 72.35 & 96.23 & 87.82 & 71.70 & 97.92 & 89.35 & 78.86 & 97.37 & 92.65 \\ FCN-32s [11] & 83.67 & 96.69 & 94.59 & 74.36 & 96.32 & 88.94 & 75.80 & 96.41 & 89.45 & 89.45 & 87.67 & 96.72 \\ FCN-16s [15] & 84.23 & 69.91 & 94.67 & 75.14  
algorithm achieves a goal state with a target reward representation and finishes in less than O(en) steps. S denotes a finite state set, and G\(\in\)S is the goal states non-empty set. A(s) is the actions finite set that are taken in execution such that s \(\in\) S.where e = \(\sum_{s}\)\(\in\) S \(\mid\) A(s) \(\mid\). Our algorithm can solve complex problems of identifying skin lesions, which is very difficult using conventional techniques. The technique achieves long-term results and corrects the error that occurs during the training process. Once our model has corrected an error, there is less chance that it occurs again. In case the training dataset is absent, the RL algorithm can learn from its experiences. It maintains a balance between exploitation and exploration, where exploration is the process of exploring the most rewarding ways to reach the target. In contrast, exploitation is searching for the most optimal solutions to see if they are better than the solution that has been tried before.