# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Results
In this study, spatial accuracy (S), accuracy (Acc) and F-measures (Fm) were considered. These metrics provide an overall view of the model performance and return values between 0 and 1 (higher values are better). Their definitions are as follows:  
\[\text{Fm}=2\frac{PR*RC}{PR+RC}\qquad S=\frac{TP}{TP+FN+FP}\qquad Acc=\frac{TP+ TN}{TP+FP+FN+TN} \tag{10}\]  
\[RC=\frac{TP}{TP+FN}\qquad PR=\frac{TP}{TP+FP} \tag{11}\]  
From these measures, the performance of the proposal was studied for each of the methods and the results are shown in Tables 2 and 3. In particular, Table 2 reports the detection performance of all considered methods, while Table 3 shows their classification performance. In both tables, the different methods considered in this work can be organized into three groups: no operation method (the baseline), methods that combine binarization and thresholding techniques, and consensus methods (that are composed of the methods from both the two previous groups). Figure 11 summarizes the detection performance of 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). As shown, the performance of the baseline method is considerably higher than that of the remaining methods that belong to the ensemble (nc5, no2, sc2, and so3). Regarding the ensemble, it considers the prediction of each method independently and merges them by using the aggregation function. Although these other methods separately yield worse results than the baseline, the ensemble offers a more accurate response compared with the baseline. Regarding chromosome detection, in general terms, it can be observed that the number of true positives is much higher than the number of false positives (unmatched predictions) and false negatives (undetected chromosomes), although these values are still significantly high. Spatial accuracy is quite high in most of the experiments, whereas F-measure seems to be better in those experiments that apply the opening morphological filter. Figure 10: Schema of the ensemble operation. In a first phase, it is shown whether each ground truth chromosome is detected by the model. Then, in a second phase, for those chromosomes that were detected, it is shown whether the model classifies correctly as dicentric or non-dicentric. The performance of the experiment applying [PERSON]'s 2 \(\times\) 2 opening stands out from the rest, but falls short of the performance of the experiment applying no operator at all. However, when the ensemble of all operators is used, it outperforms the experiment that does not apply any operator. It must be highlighted that the performance yielded by the consensus respects that achieved by the detector applied directly over the raw images (the traditional and simplest strategy to face a detection problem), which corresponds with the \"No operation\" row. As reported for \"Consensus 0.1\" and \"Consensus 0.2\", both proposals outperform the \"No operation\" method.