Dendritic neural networks in the classification of estrous cycle images


  • Rocio Ochoa-Montiel Instituto Politécnico Nacional - CIC. Universidad Autónoma de Tlaxcala,
  • Rodrigo Román-Godínez Instituto Politécnico Nacional - CIC
  • Erik Zamora Instituto Politécnico Nacional - CIC
  • Juan Humberto Sossa Azuela Instituto Politécnico Nacional
  • Gerardo Hernández Universidad Autónoma de la Ciudad de México


Dendrite neural networks, Multi-Layer Perceptron, Estrous cycle


In the biological area, the short reproductive

cycle in rodents is useful because it allows analyzed

electrophysiological properties, behaviors, or drug effects through the changes observed during this period. This cycle is composed of 4 stages in which the classification is determined by vaginal cytology.

Although automatic approaches have been used for the classification of these stages, they are computationally expensive and require a great number of images for adequate performance.

In this paper, we test different models of dendritic neural networks (DNN) trained by stochastic gradient descent to classify a short number of images and four classical contrast enhancement methods. We extract texture features and use standard and DNN classifiers to recognize the images.

From the experiments, it seems that DNNs have a more stable behavior concerning the standard classifiers according to the standard deviation presented, being this a desirable property for a model. We consider that DNN could be an adequate alternative for the classification of estrous cycle images.




How to Cite

Ochoa-Montiel, R. ., Román-Godínez, R. ., Zamora, E. ., Sossa Azuela, J. H., & Hernández, G. (2023). Dendritic neural networks in the classification of estrous cycle images. International Journal of Combinatorial Optimization Problems and Informatics, 14(1), 39–48. Retrieved from




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