## 4 Results and Discussions
### Visual Results of the Segmentation  
Despite the segmentation model presenting encouraging results, some images in the test set were segmented incorrectly. Figure 7 illustrates some images verified by the U-Net network. In Figure 7, the first column presents clippings of the original images, the second column the segmentation masks made by the specialists, and the third column is the result of the segmentation carried out with the U-Net network. The red line highlights the expert notes. Figure 6: Learning Curves of the U-Net Architecture. The marking (x) in red highlights the time when the model obtained the lowest validation error. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\
\begin{tabular}{c} **Number** \\ **of Images** \\ \end{tabular} } & \multirow{2}{*}{**Dice**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**Acc**} & \multirow{2}{*}{**Prec**} & \multirow{2}{*}{**Sen**} & \multirow{2}{*}{**Spe**} & \multirow{2}{*}{**AUC**} \\ \hline [PERSON] (2011) [18] & 794 & - & - & 0.943 & - & - & - & - \\ [PERSON] and Teofilo (2012) [22] & 794 & - & - & 0.943 & - & - & - & - \\ [PERSON] and Teofilo (2012) [23] & 794 & - & - & 0.949 & - & - & - & - \\ [PERSON] (2013) [25] & 10 & - & - & - & 0.868 & 0.871 & - & - \\ [PERSON] (2014) [12] & 44 & - & - & - & 0.815 & 0.876 & - & - \\ [PERSON] (2014) [29] & 40 & - & - & 0.304 & 0.855 & 0.266 & - & - \\ [PERSON] (2016) [30] & 40 & - & - & 0.700 & - & - & - & - \\ [PERSON] (2018) [17] & 45 & 0.777 & - & - & 0.757 & 0.823 & - & - \\ [PERSON] (2019) [35] & 45 & 0.850 & - & - & - & - & - \\ [PERSON] (2020) [38] & - & - & - & 0.950 & - & - & - & - \\ \hline \multirow{3}{*}{Proposed method} & \multicolumn{2}{c}{78} & \multirow{3}{*}{
\begin{tabular}{c} 78 \\ 49,141 (PC) \\ 44,603 (NC) \\ \end{tabular} } & \multicolumn{2}{c}{0.804} & \multicolumn{2}{c}{0.752} & \multicolumn{2}{c}{0.991} & \multicolumn{2}{c}{0.815} & \multicolumn{2}{c}{0.722} & \multicolumn{2}{c}{0.996} & \multicolumn{2}{c}{0.859} \\ \cline{1-1} \cline{2-10} \cline{5-10} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}  
* Acc: Accuracy; Prec: Precision; Sen: Sensitivity; Spe: Specificity; PC: Positive clippings; NC: Negative clippings.  
\end{table} 
Table 6: Performance of the proposed detection method compared to the state of the art. Figure 7: _Cont_. Based on Figure 7, the image Figure 7a was correctly segmented, identifying the cluster of amastigotes in the image. The image Figure 7b shows a _Leishman_ annotated by the expert and located under the edge of the clipping, but the segmentation model did not recognize its region. Image Figure 7c has no parasites noted in the clipping, but a region was erroneously segmented. The image Figure 7d was correctly interpreted by the segmentation model, as it did not detect amastigotes in the image. During the generation of clippings in a 96 \(\times\) 96 pixel window, there were some situations in which the amastigote was located under the edge of the clipping and had an area smaller than 50% inside the clipping. For these cases, the _Leishman_ annotation was excluded from the clipping mask. This exclusion was necessary to prevent small \"pieces\" of _Leishman_ from being read by U-Net, and with that, the false positives generated by the segmentation model were reduced. Due to this pre-processing, the segmentation model recognized some amastigotes with large area sizes, as illustrated in Figure 8.  
After empirical analysis of the segmentation results, the amastigotes recognized by U-Net present a better outline of the parasite region when compared to the specialist's manual marking. This is because the U-Net has texture and edge markers with higher precision. The parasites were marked in the images using the LabelMe tool, proposed by [PERSON] et al. (2008) [PERSON] et al. (2008).