# 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
This method is well balanced because there is a high degree of separation between the foreground and the background. The proposed number of parameters in a model, storage measurements, and inference speed is compared to other state-of-the-art models. The GN used in previous work tests the mean and variation of the channel groups since they have been standardized, e.g., the AG U-Net model scans images in epochs that can easily connect to other algorithms. In BCDU, the image upload epoch is 359 s, while the U-Net baseline is 165 s. It takes 133 s to encrypt a terabyte of info. For the NVIDIA Quadro K1200 GPU, the proposed solution's predicted performance (AG U-Net + GN + TL) is faster than the U-Net baseline with a 256 \(\times\) 256 input scale [75]. Figure 8: The ablation experiment segmentation diagrams for the input image. (**a**) The initial image taken from the dataset. (**b**) The image’s ground truth; (**c**) Att U-Net + GN + TL; (**d**) Att U-Net + GN + FTL; (**e**) Att U-Net + GN + DL (TL: Tversky Loss; FTL: focal Tversky loss; DL = Dice loss). (**f**) Our RL algorithm results. The segmentation mask boundaries are sharper than the state-of-the-art methods, as shown. The visual segmentation results in Figures 9-12 demonstrate the proposed RL method's better segmentation performance than the other state-of-the-art methods. The performance metrics visualization for the PH2 and the ISIC 2017 challenge dataset is shown in Figure 12. The statistical measures used by our methods, such as _Acc_, Dice, Jaccard index, _Sen_, and specificity 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 Figure 12b, 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 denote the PH2 and the ISIC 2017 dataset metrics, the highlighted points indicate the metric values such as _Sen_, _Spe_, and _Acc_ for each of the categories on both datasets. Figure 12c shows the action bundle effect. Figure 9: The visual projections derived from the ISIC 2018 image dataset. (**a**) The original image from the dataset. (**b**) The given binary mark (ground truth), (**c**) The prediction effects of the proposed method (Att U-Net + GN). (**d**) The results of our RL algorithm. Our algorithm is able to segment the lesions clearly. However, in some cases, it shows exactly the lesion shape in the segmentation mask, deviating a bit from the ground truth. However, as can be seen, the segmentation masks are clear. The Dice index values taken for the ablation experiments are shown in Table 1. 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. In Table 2, our approach works better for _Sen_ and _Spe_ than all of the algorithms, with values 98.59% and 97%. We aim to design these algorithms for high _Sen_ and _Spe_. The _Sen_ value is larger than other methods in terms of various statistical parameters taken for comparison. Table 2 compares the proposed model's segmentation efficiency to that of state-of-the-art methods on the PH2 dataset in terms of _Dice index_, _Jaccard index_, _Acc_, _Sen_, and _Spe_. The _Acc_, Jaccard index, and _Spe_ values are 0.96, 0.92, and 0.97. The higher _Acc_ shows that the ratio of the correctly segmented area over the ground truth is higher with respect to other methods. The percent overlap between the target mask and the predicted mask is higher. Table 3 shows the results of our suggested approaches and state-of-the-art algorithms on the ISIC 2017 dataset.