# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Results
It has been observed that, for most subdivisions of the dataset from all the methods, the loss function tends to decrease over epochs (see Figure 6) for both the detection and classification of chromosomes into dicentric or non-dicentric. This behaviour occurs in both the training and validation processes, so the model does not exhibit overfitting characteristics. Likewise, it is also noteworthy that the precision, completeness and mAP50 functions tend to grow over epochs, which is a remarkable aspect of the model performance. However, it should be noted that the values reached in the last epoch are not always as high as desired. It is interesting to observe how the evaluation curve of the mAP50 indicator shows abnormal fluctuations. The reason for this is that the dataset exhibits a strongly unbalanced class distribution besides a high variance in object sizes, a low number of samples, and low-resolution images. These facts raised difficulties in accurately classifying dicentric chromosomes, impacting the evaluation metric throughout the training. An example of the predictions is shown in Figure 7. As illustrated, all the chromosomes are well detected, but not well classified. Figure 6: Model performance for the \(2\times 2\) Spline closing method. It can be noted that loss function decreases over the epochs, whereas the recall, precision and mAP50 metrics increase. From a qualitative point of view, it could be stated that the models, in general, have not presented problems in the detection of chromosomes and the predictions showed a high correspondence with the bounding boxes of the ground truth. As far as classification is concerned, each experiment showed better or worse results depending on the image being examined. Figures 8 and 9 compare their performance in the recognition of a dicentric (anomalous) chromosome and a non-dicentric (non-anomalous, or normal) chromosome, respectively. In Figure 8, it can be seen that a specific dicentric chromosome is detected and classified as dicentric in most experiments. Figure 9 shows a monocentric chromosome that was detected and classified as undamaged in most experiments. The results achieved by our methodology have been interpreted in two phases: detection and classification. A schematic diagram is given in Figure 10.  
In the detection phase, we analyzed whether the chromosomes were detected by the model regardless of their class. To consider that a chromosome has been detected, a condition was imposed requiring a prediction with an IoU (Intersection over Union) greater than 0.7. Subsequently, analysis of the count of detected chromosomes (true positives), undetected chromosomes (false negatives) and uncorrelated predictions (false positives) was carried out. In the classification phase, we studied whether the chromosomes detected were correctly classified by the model. Four cases were recorded: anomalous chromosomes predicted as such (true positives), non-anomalous chromosomes predicted as such (true negatives), anomalous chromosomes predicted as non-anomalous chromosomes (false negatives) and non-anomalous chromosomes predicted as anomalous chromosomes (false positives). Figure 7: Prediction for the chromosomes of the image 2 Gy-329 for the experiment where the Spline thresholding technique with \(2\times 2\) closing filter was employed. In green: predictions as ‘non-dicentric’, in red: predictions as ‘dicentric’. Figure 8: Composition of 2 Gy-329 image predictions for a dichentric chromosome. In green: predictions as ‘non-dicentric’; in red: predictions as ‘dicentric’. Figure 9: Composition of 2 Gy-329 image predictions for a non-dicentric chromosome. In green: predictions as ‘non-dicentric’; in red: predictions as ‘dicentric’. Subsequently, a data assembly (ensemble) was performed to evaluate the results obtained and to determine a consensus function that will enhance the overall performance. To establish a fair comparison between the tested models, different well-known metrics were selected.