# 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
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & _Acc_ & _Dice Score_ & _Jaccard Index_ & _Sen_ & _Spe_ \\ \hline First: Yading & & & & & \\ Yuan (CDNN & 0.934 & 0.849 & 0.765 & 0.825 & 0.975 \\ model) [35] & & & & & \\ Second: Matt & & & & & \\ Berseth (U- & 0.932 & 0.847 & 0.762 & 0.820 & 0.978 \\ Net) [37] & & & & & \\ U-Net [36] & 0.901 & 0.763 & 0.616 & 0.672 & 0.972 \\ SegNet [38] & 0.918 & 0.821 & 0.696 & 0.801 & 0.954 \\ FrCN [47] & 0.940 & 0.870 & 0.771 & 0.854 & 0.967 \\ Ensemble-S [75] & 0.933 & 0.844 & 0.760 & 0.806 & 0.979 \\ [PERSON] et al. [16] & 0.939 & 0.866 & 0.788 & 0.877 & 0.955 \\ [PERSON] et al. [44] & 0.941 & 0.871 & 0.793 & 0.899 & 0.950 \\ SLSNet [76] & 0.944 & 0.875 & 0.777 & 0.841 & 0.953 \\ [PERSON] et al. [77] & 0.941 & 0.867 & 0.790 & 0.892 & 0.939 \\ [PERSON] et al. [78] & 0.873 & 0.898 & 0.829 & 0.590 & 0.941 \\ [PERSON] et al. [79] & 0.938 & 0.877 & 0.802 & 0.862 & 0.963 \\ Our RL algorithm (proposed) & 0.9539 & 0.957 & 0.840 & 0.950 & 0.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results comparison of our proposed RL method and the state-of-the-art methods on the ISIC skin lesion segmentation challenge 2017 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & _Dice Score_ & _Jaccard Index_ & _Acc_ & _Sen_ & _Spe_ \\ \hline U-Net [36] & 0.89 & 0.81 & 0.94 & 0.93 & 0.94 \\ U-Net (all 64 filters) [37] & 0.90 & 0.81 & 0.94 & 0.93 & 0.95 \\ SE\_U-Net [51] & 0.91 & 0.83 & 0.95 & 0.89 & 0.96 \\ BCDU [52] & 0.90 & 0.82 & 0.94 & 0.94 & 0.95 \\ Attn\_U-Net+GN [75] & 0.91 & 0.83 & 0.95 & 0.94 & 0.95 \\ FCN-16s [15] & 0.88 & 0.80 & 0.91 & 0.93 & 0.88 \\ DeepLab V3+ [51] & 0.89 & 0.81 & 0.92 & 0.94 & 0.89 \\ Mask R-CNN [48] & 0.90 & 0.83 & 0.93 & 0.96 & 0.89 \\ Ensemble-S [75] & 0.93 & 0.90 & 0.83 & 0.96 & 0.92 \\ [PERSON] et al. [16] & 0.88 & 0.80 & 0.92 & 0.98 & 0.86 \\ [PERSON] et al. [44] & 0.88 & 0.80 & 0.91 & 0.98 & 0.85 \\ SLSNet [76] & 0.90 & 0.81 & 0.94 & 0.87 & 0.95 \\ [PERSON] et al. [77] & 0.87 & 0.79 & 0.94 & 0.88 & 0.95 \\ [PERSON] et al. [78] & 0.89 & 0.82 & 0.87 & 0.62 & 0.94 \\ [PERSON] et al. [79] & 0.88 & 0.80 & 0.93 & 0.86 & 0.96 \\ Our RL algorithm (proposed) & 0.94 & 0.92 & 0.96 & 0.9859 & 0.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The proposed RL model’s segmentation efficiency is compared to state-of-the-art methods. The statistical measures such as Dice score, Jaccard index, _Acc_, _Sen_, and _Spe_ are evaluated on the PH2 dataset. state-of-the-art methods. From this, we infer that we can qualitatively and quantitatively segment skin lesions better than the other approaches. Our findings are also compared to U-Net [36], SE U-Net [37], BCDU [52], DeepLab V3+ [51], and other methods. The SE U-Net [35] approach works in a manner analogous to the proposed technique as it simplifies the inter-class dependencies by adding several convolution layer parameters. However, it is unable to perform the segmentation of minor and complicated lesions. The value of RVD is negative and has a value of -0.0451, which signifies our better performance compared to the other methods. In Tables 4 and 5, we compare our RL algorithm on three different lesion types: seborrheic keratosis (SK), naevus, and melanoma. It is compared with methods such as FCN-AlexNet [10], FCN16s [15], FCN 8s [41], etc. In Table 4, the algorithm outperforms other methods with an overall value of the Dice index as 93.98%, JSI as 88.79%, and MCC of 90.21%. The higher Dice and Jaccard index values indicate the better similarity of our qualitative results in comparison with the ground truth images. Table 5 shows the overall values for the various categories of the lesions are 96.25% for _Sen_, _Spe_ as 94.71%, and _Acc_ as 95.33%. Thus, we conclude that our RL algorithm on the benchmark datasets has enhanced the segmentation _Acc_, thus outperforming the state-of-the-art methods, increasing 7%, 8%, 9%, respectively, for Dice index, specificity, and Jaccard index, respectively. The other statistical measures are also higher for our RL methods.