# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 4 Conclusions  
The application of artificial intelligence techniques in the detection of dicentric chromosomes is a promising approach in the field of biological dosimetry, as AI-based tools have the capacity to improve the detection of dicentric chromosomes as well as the dose prediction accuracy when compared to laborious manual scoring. The automated detection of dicentric chromosomes can also drastically reduce inter-laboratory variations arising from discrepancies in dicentric chromosome scoring by laboratory personnel. Our current work has focused on the use of convolutional neural networks to detect dicentric chromosomes in images of metaphase cells prepared from human lymphocytes. The images were preprocessed using different thresholding techniques, morphological filters, and filter sizes, and a model detector was trained for each of the selected operators. Then, we proceeded to apply data assembly techniques to enhance the performance of the model due to the low number of samples in the considered dataset. In this way, the same set of sample images was transformed into different datasets through several different transformations, and then different models were learned, which were finally evaluated by ensemble learning. The performance, both in the process of chromosome detection and in the process of classification into dicentric or non-dicentric, was examined. Our study convincingly demonstrated that the approach applying the opening filters yielded significantly better results. The application of a detector over the raw dataset offers the best performance with respect to the individual performance achieved by each method; however, the use of an ensemble slightly surpasses that performance. The dataset poses significant challenges due to its limited number of samples, highly imbalanced class distribution, considerable variance in object sizes, and low image resolution. In fact, several dicentric chromosomes within it are so difficult to detect that even human experts in the field struggle. Consequently, these sample size limitations negatively impact the model's generalizability. Future experiments are needed for optimizing and developing AI-based tools to expedite the biodosimetry-based triage process for prioritizing medical treatments to save the lives of individuals exposed to high doses of IR. In particular, future research should aim to expand the dataset to include a more diverse range of samples, thereby enhancing the robustness and generalizability of the models. To achieve this, data augmentation techniques may be explored. Super-resolution, generating more detailed images, could also help to improve the detection and classification accuracies, besides exploring alternative neural network architectures and modern techniques, which could yield new insights. Conceptualization, M.J.R.-G., F.S.-P. and A.M.; methodology, M.A.M.-C.; software, I.A.-J. ; validation, M.A.M.-C.; formal analysis, L.A.-J. and M.A.M.-C.; investigation, L.A.-J., A.S.B., M.J.R.-G., F.S.-P. and A.M.; resources, A.S.B., M.J.R.-G., F.S.-P., A.M. and M.A.M.-C.; data curation, A.S.B. and I.A.-J. ; writing--original draft preparation, I.A.-J., A.S.B., M.J.R.-G., A.M. and M.A.M.-C.; writing--review and editing, I.A.-J., A.S.B., M.J.R.-G., F.S.-P., A.M. and M.A.M.-C.; visualization, I.A.-J. ; supervision, M.A.M.-C.; project administration, M.J.R.-G. and M.A.M.-C.; funding acquisition, M.A.M.-C. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the Autonomous Government of Andalusia (Spain) under grant UMA20-FEDERJA-108, the Ministry of Science and Innovation of Spain under grant PID2022-136764 OA-I00, the University of Malaga (Spain) under grants B4-2022, B1-2021_20 and B1-2022_14, and the Fundacion Unicaja under grant PUNI-003_2023. The dataset used in this study is available on reasonable request from the corresponding author.