Publications

@article{alaiz-rodriguez_quantifying_2008,

title = {Quantifying the proportion of damaged sperm cells based on image analysis and neural networks},

author = {Rocıo Alaiz-Rodrıguez and Enrique Alegre and Vıctor González-Castro and Lidia Sánchez},

url = {https://www.researchgate.net/profile/Victor-Gonzalez-Castro/publication/234818790_Quantifying_the_proportion_of_damaged_sperm_cells_based_on_image_analysis_and_neural_networks/links/0912f5130e6b0a202c000000/Quantifying-the-proportion-of-damaged-sperm-cells-based-on-image-analysis-and-neural-networks.pdf},

year  = {2008},

date = {2008-01-01},

journal = {Proceedings of SMO},

volume = {8},

pages = {383–388},

abstract = {This paper addresses the challenge of quantifying the proportion of damaged and intact sperm cells in a sample using computer vision techniques and supervised learning. A novel approach based on Posterior Probability (PP) estimates is introduced to improve classifier accuracy despite changes in class distributions. The PP-based quantification outperforms traditional methods, such as Adjusted Count, Median Sweep, and the naive counting approach, in terms of Mean Absolute Error, Kullback Leibler divergence, and Mean Relative Error. This approach ensures consistent accuracy regardless of class distribution variations.},

keywords = {Class Distribution Estimation, neural networks, Quantification Methods, Semen Image Analysis},

pubstate = {published},

tppubtype = {article}

}