# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c|c c c c} \hline \hline
**Method** & **TP** & **FP** & **FN** & **RC** & **PR** & **FM** & **S** \\ \hline No operation & 2256 & 2 & 83 & 0.96 & 1.00 & 0.49 & 0.96 \\ nc2 & 2004 & 227 & 335 & 0.86 & 0.90 & 0.44 & 0.78 \\ nc3 & 2139 & 854 & 200 & 0.91 & 0.71 & 0.40 & 0.67 \\ nc4 & 1976 & 700 & 363 & 0.84 & 0.74 & 0.39 & 0.65 \\ nc5 & 2004 & 0 & 335 & 0.86 & 1.00 & 0.46 & 0.86 \\ no2 & 2144 & 7 & 195 & 0.92 & 1.00 & 0.48 & 0.91 \\ no3 & 2086 & 2 & 253 & 0.89 & 1.00 & 0.47 & 0.89 \\ no4 & 2139 & 51 & 200 & 0.91 & 0.98 & 0.47 & 0.89 \\ no5 & 1956 & 1987 & 383 & 0.84 & 0.50 & 0.31 & 0.45 \\ sc2 & 1836 & 100 & 503 & 0.78 & 0.95 & 0.43 & 0.75 \\ sc3 & 1693 & 271 & 646 & 0.72 & 0.86 & 0.39 & 0.65 \\ sc4 & 1030 & 8 & 1309 & 0.44 & 0.99 & 0.31 & 0.44 \\ sc5 & 799 & 1794 & 1540 & 0.34 & 0.31 & 0.16 & 0.19 \\ so2 & 1804 & 210 & 535 & 0.77 & 0.90 & 0.41 & 0.71 \\ so3 & 1887 & 51 & 452 & 0.81 & 0.97 & 0.44 & 0.79 \\ so4 & 1791 & 186 & 548 & 0.77 & 0.91 & 0.41 & 0.71 \\ so5 & 1808 & 1 & 531 & 0.77 & 1.00 & 0.44 & 0.77 \\ \hline Consensus 0.05 & 2330 & 6450 & 9 & 1.00 & 0.27 & 0.42 & 0.27 \\ Consensus 0.1 & 2314 & 0 & 25 & 0.99 & 1.00 & 0.99 & 0.99 \\ Consensus 0.2 & 2278 & 0 & 61 & 0.97 & 1.00 & 0.99 & 0.97 \\ Consensus 0.3 & 2211 & 0 & 128 & 0.95 & 1.00 & 0.97 & 0.95 \\ Consensus 0.4 & 2159 & 0 & 180 & 0.92 & 1.00 & 0.96 & 0.92 \\ Consensus 0.5 & 2012 & 0 & 327 & 0.86 & 1.00 & 0.92 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the detection of chromosomes. The first column indicates the operator (‘n’ and ‘s’ stand for Otsu and Spline techniques, respectively; ‘c’ and ‘o’ stand for closing and opening morphological filter, respectively; the value next to consensus means the minimum percentage of coincidence between the ensemble’s members to be considered for each predicted detection) and the remaining columns show the yielded mean performance. Figure 11: Detection performance (spatial accuracy) achieved by the baseline method (no operation), the best methods for each combination of binarization technique and morphological filter (nc5, no2, sc2 and so3), and the best ensemble methods (consensus 0.1, 0.2 and 0.3). With respect to the classification task, the models yielded a greater number of true predictions than false predictions for the chromosomes that were detected, with a remarkable majority of predictions corresponding to true negatives (correctly classified non-dicentrics). However, it is notable that the models faced difficulties characterizing the dicentric chromosomes. For example, in Figure 12, a dicentric chromosome was classified incorrectly in most of the experiments. There are significantly more false negatives (dicentrics that were not classified as such) than true positives (correctly classified dicentrics) in all the experiments. This drawback is evident in the metrics of the classification phase, in which the values for spatial accuracy and F-measure are not high enough for any of the methods. It must be highlighted that there is no method that surpasses the performance achieved by the no-operation operator. However, when the ensemble was used, a higher number of dicentric chromosomes was correctly classified. These results might be due to the reduced rate of dicentric chromosomes relative to undamaged monocentric chromosomes in the images. This is presumably a stumbling block for the approach in the task of extracting the patterns that allow the identification of dicentric chromosomes. Likewise, if the initial dataset had a larger volume of images or the images had a higher resolution, perhaps the model might have performed better in recognizing the patterns that allow the classification of dicentric chromosomes. It should also be noted that this task by itself is a challenge for health professionals in distinguishing a dicentric chromosome from a monocentric chromosome when the chromatids are simply superimposed, which will negatively influence the analysis and interpretation.