Dendritic neural networks in the classification of estrous cycle images

Abstract

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.