# 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 c} \hline \hline
**Method** & **TP** & **TN** & **FP** & **FN** & **RC** & **PR** & **FM** & **S** & **ACC** \\ \hline No operation & 26 & 2178 & 6 & 46 & 0.36 & 0.81 & 0.50 & 0.33 & 0.98 \\ nc2 & 12 & 1936 & 8 & 48 & 0.20 & 0.60 & 0.30 & 0.18 & 0.97 \\ nc3 & 11 & 2068 & 9 & 51 & 0.18 & 0.55 & 0.27 & 0.15 & 0.97 \\ nc4 & 9 & 1871 & 39 & 57 & 0.14 & 0.19 & 0.16 & 0.09 & 0.95 \\ nc5 & 12 & 1936 & 8 & 48 & 0.20 & 0.60 & 0.30 & 0.18 & 0.97 \\ no2 & 14 & 2068 & 9 & 53 & 0.21 & 0.61 & 0.31 & 0.18 & 0.97 \\ no3 & 14 & 2026 & 2 & 44 & 0.24 & 0.88 & 0.38 & 0.23 & 0.98 \\ no4 & 14 & 2062 & 10 & 53 & 0.21 & 0.58 & 0.31 & 0.18 & 0.97 \\ no5 & 10 & 1889 & 8 & 49 & 0.17 & 0.56 & 0.26 & 0.15 & 0.97 \\ sc2 & 6 & 1778 & 4 & 48 & 0.11 & 0.60 & 0.19 & 0.10 & 0.97 \\ sc3 & 5 & 1634 & 9 & 45 & 0.10 & 0.36 & 0.16 & 0.08 & 0.97 \\ sc4 & 10 & 987 & 3 & 30 & 0.25 & 0.77 & 0.38 & 0.23 & 0.97 \\ sc5 & 5 & 768 & 4 & 22 & 0.19 & 0.56 & 0.28 & 0.16 & 0.97 \\ so2 & 8 & 1727 & 17 & 52 & 0.13 & 0.32 & 0.19 & 0.10 & 0.96 \\ so3 & 9 & 1812 & 17 & 49 & 0.16 & 0.35 & 0.21 & 0.12 & 0.97 \\ so4 & 9 & 1731 & 1 & 50 & 0.15 & 0.90 & 0.26 & 0.15 & 0.97 \\ so5 & 11 & 1742 & 8 & 47 & 0.19 & 0.58 & 0.29 & 0.17 & 0.97 \\ \hline Consensus 0.05 & 35 & 2167 & 76 & 52 & 0.40 & 0.32 & 0.35 & 0.21 & 0.95 \\ Consensus 0.1 & 25 & 2203 & 25 & 61 & 0.29 & 0.50 & 0.37 & 0.23 & 0.96 \\ Consensus 0.2 & 16 & 2187 & 8 & 67 & 0.19 & 0.67 & 0.30 & 0.18 & 0.97 \\ Consensus 0.3 & 11 & 2135 & 1 & 64 & 0.15 & 0.92 & 0.25 & 0.14 & 0.97 \\ Consensus 0.4 & 6 & 2089 & 1 & 63 & 0.09 & 0.86 & 0.16 & 0.09 & 0.97 \\ Consensus 0.5 & 5 & 1952 & 0 & 55 & 0.08 & 1.00 & 0.15 & 0.08 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the classification 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. In contrast, no notable differences was observed in general terms between the performance presented by experiments that employed the Spline thresholding technique versus those that used the Otsu thresholding technique. In addition, it was also previously theorized that opening filters could lead to an increase in the number of false positives, while closing filters could lead to an increase in false negatives. It has been observed that, in the experiments that applied the Otsu thresholding technique, the number of false negatives was slightly higher in the experiments that applied closing filters versus those that applied opening filters, but no significant differences were observed in the number of false positives between opening and closing filters. Likewise, in experiments that applied the Spline thresholding technique, the number of false positives was slightly higher in experiments that applied opening filters versus those that applied closing filters, but no significant differences in the number of false negatives were observed between opening and closing filters. Besides these obtained results and their analysis, it is remarkable that the dataset is highly challenging: it presents a low number of samples, a strongly imbalanced class distribution, a high variance in object sizes, and low-resolution images. In fact, several dicentric chromosomes from it are very difficult to detect, even by human experts in this field. Therefore, this sample size limitation offered by the dataset negatively affects the model's generalizability. The main goal of this study was to develop an AI-based tool for the efficient detection of dicentric chromosomes which can expedite the dose estimation process and thereby enable a rapid triage.