# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Results
After that, only those detections with at least a certain percentage of coincidence between the ensemble's members were considered. Therefore, if a prediction was not provided by a certain percentage of the ensemble's members, that prediction was filtered out and not used within the prediction provided by the ensemble. In this way, the aggregation function \(\phi\), which defines the consensus strategy used in these experiments, is a combination of the predictions provided by the ensemble methods where the detection prediction is filtered if a minimum percentage of coincidence between the ensemble's members is not surpassed. That minimum percentage of coincidence between the ensemble's members is noted as \(quota\). In this way, for example, \"Consensus 0.10\" returns a detection prediction if at least 10% of the members detect it. Respecting the consensus classification prediction for a given predicted detection, it is computed as the average of the classification predictions for that detection. The detections to be merged were those provided by each combination of binarization technique (Otsu \begin{table}
\begin{tabular}{l p{284.5 pt}} \hline \hline
**Parameter** & **Description** \\ \hline \(v_{min}\) & Minimum confidence threshold for detections. The detector disregards those objects detected with a confidence below this threshold. The parameter is noted as \(conf\) in YOLOv8x (default value of 0.25). In this work, \(v_{min}=0.7\). \\ \(iou\_detector\) & Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Parameter used by YOLOv8x noted as \(iou\) with a default value of 0.7. \\ \hline \(\phi\) & The aggregation function of the ensemble. In this work, it combines the prediction provided by the different methods of which the ensemble is composed. That combination disregards those objects with an IoU below the \(iou\_ensemble\) threshold and those predictions with at least a certain percentage of coincidence between the ensemble’s members (\(quota\)). IoU threshold for consensus merged prediction. The ensemble disregards those merged objects detected with IoU below this threshold. In this work, \(iou\_ensemble=0.5\). The ensemble disregards those merged objects detected if the percentage of coincidence between the ensemble’s members is below this threshold. In this work, \(quota=\{0.05,0.1,0.2,0.3,0.40.5\}\). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Tuned configurations for the parameters of the proposal. and Spline), morphological operator (opening and closing filters) and size of filter (\(2\times 2\), \(3\times 3\), \(4\times 4\), and \(5\times 5\)). Respecting the percentage of coincidence between the ensemble's members, different values were tested: 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5. It must be emphasized that the balance between complexity and accuracy must be managed critically when an ensemble strategy is proposed. Generally, the higher the number of networks in the ensemble, the better the system's detection performance, while a lower number of networks leads to a faster system. However, the optimal number of networks in an ensemble may vary greatly depending on different factors. For example, the specific problem to be addressed is crucial for determining which kind of system will be suitable: a sensitive issue (for example, evaluating medical images) may require the highest possible detection performance, while a real-time problem (for example, estimating traffic flow from road video sequences) may require a fast output. In our work, to achieve the highest possible detection performance, we considered a high number of networks within the ensemble. This number is the result of each combination of binarization technique (Otsu and Spline (i.e., two possibilities)), morphological operator (opening and closing filters (two possibilities)) and size of filter (\(2\times 2\), \(3\times 3\), \(4\times 4\), and \(5\times 5\) (four possibilities)), plus the non-operation model, resulting in an ensemble composed of 17 networks (thus, M = 17).