## 4 Results and Discussions  
As previously explained, the available image base has amastigote markings. In this way, it was possible to carry out the segmentation experiments. The clippings generated from the 78 positive images for VL were used in this experiment. These clippings were divided into 70% for training, 10% for validation, and 20% for testing, as described in Table 3.  
Due to the size of the amastigotes about the original size of the slide field images, the images were divided into clippings to use the U-Net methodology. Cutouts of size 96 \(\times\) 96 were performed, with a dynamic step that varied based on the presence or absence of the amastigote. The segmentation evaluation stage uses metrics from the literature, such as the Jaccard index (IoU), Dice, Accuracy, Precision, Sensitivity, Specificity, and area under the ROC curve (AUC) [44]. Table 5 presents the results of the main segmentation experiments. The experiments were carried out based on the U-Net architecture. The results obtained for the best segmentation model were a Dice of 0.804, IoU of 0.752, and AUC of 0.859. Also, False Positive Rate (FPR) = 0.004 and False Negative Rate (FNR) = 0.278. Dice, IoU, and AUC targeting metrics assess targeting quality. The Dice coefficient measures the overlap between the targeted and true regions, with higher values indicating more accurate segmentation. The IoU calculates the proportion of the intersection over the union between the segmented and true regions, being a measure of similarity between them. The AUC is a metric used in classification tasks and represents the area under the ROC curve, assessing \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dimensions**} & **U-Net** & **DA** & \multirow{2}{*}{**Color**} & \multirow{2}{*}{**Dice**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**Acc**} & \multirow{2}{*}{**Prec**} & \multirow{2}{*}{**Sen**} & \multirow{2}{*}{**Spe**} & \multirow{2}{*}{**AUC**} \\  & **Filters** & & & & & & & & & & \\ \hline \(512\times 512\) & 32 & - & RGB & 0.631 & 0.461 & **0.999** & 0.663 & 0.601 & **0.999** & 0.800 \\ \hline \(256\times 256\) & 32 & - & RGB & 0.726 & 0.569 & 0.993 & 0.849 & 0.634 & 0.998 & 0.816 \\ \(256\times 256\) & 64 & - & RGB & 0.742 & 0.589 & 0.993 & 0.851 & 0.657 & 0.998 & 0.828 \\ \hline \(128\times 128\) & 32 & - & RGB & 0.726 & 0.661 & 0.991 & **0.864** & 0.644 & 0.998 & 0.821 \\ \(128\times 128\) & 32 & - & LUV & 0.674 & 0.608 & 0.991 & 0.853 & 0.704 & 0.998 & 0.850 \\ \(128\times 128\) & 64 & - & RGB & 0.762 & 0.694 & 0.991 & 0.860 & 0.665 & 0.998 & 0.832 \\ \(128\times 128\) & 64 & - & LUV & 0.772 & 0.706 & 0.991 & 0.862 & 0.657 & 0.998 & 0.828 \\ \(128\times 128\) & 64 & - & LAB & 0.749 & 0.598 & 0.991 & 0.856 & 0.665 & 0.998 & 0.831 \\ \(128\times 128\) & 64 & 0.1 & RGB & 0.782 & 0.715 & 0.992 & 0.821 & 0.738 & 0.997 & 0.867 \\ \(128\times 128\) & 64 & 0.2 & RGB & 0.734 & 0.670 & 0.990 & 0.859 & 0.609 & 0.998 & 0.803 \\ \hline \(96\times 96\) & 32 & 0.1 & LUV & 0.645 & 0.591 & 0.989 & 0.797 & **0.757** & 0.995 & **0.876** \\ \(96\times 96\) & 32 & 0.1 & RGB & 0.703 & 0.650 & 0.988 & 0.826 & 0.748 & 0.995 & 0.871 \\ \(96\times 96\) & **64** & **0.1** & **RGB** & **0.804** & **0.752** & 0.991 & 0.815 & 0.722 & 0.996 & 0.859 \\ \hline \hline \end{tabular} U-Net filters: number of initial filters of the U-Net architecture, DA Brightness: brightness variation and/or contrast in data augmentation; Acc: Accuracy; Prec: Precision; Sen: Sensitivity; Spe: Specificity; () Untested values; The bold highlight represents the best result.  
\end{table} 
Table 5: Main results of segmentation experiments. the ability to discriminate between classes in segmentation. The FPR metric indicates the background regions incorrectly segmented as amastigotes, and the FNR measure indicates the regions containing amastigotes and incorrectly identified as image backgrounds. Among the experiments carried out, it was noticeable that some hyperparameters are more sensitive than others.