Estrous Cycle Classification through Automatic Feature Extraction

Gerardo Hernández Hernández, Leonardo Delgado Toral, María del Rocío Ochoa Montiel, Erik Zamora Gómez, Juan Humberto Sossa Azuela, Aldrín Barreto Flores, Francisco Ramos Collazo, Rosalina María de Lourdes Reyes Luna


We study and propose, for the first time, an autonomous classification of the estrous cycle (the reproductive cycle in rats). This cycle consists of 4 stages: Proestrus, Estrus, Metestrus and Diestrus. The short duration of the cycle in rats makes them an ideal model for research about changes that occur during the reproductive cycle. Classification is based on the cytology shown by vaginal smear. For this reason, we use manual and automatic feature extraction; these features are classified with support vector machines, multilayer perceptron networks and convolutional neural networks. A dataset of 412 images of estrous cycle was used. It was divided into two sets. The first contains all four stages, the second contains two classes. The first class is formed by the stages Proestrus and Estrus and the second class is formed by the stages Metestrus and Diestrus. The two sets were formed to solve the main problems, the research of the reproductive cycle and the reproduction control of rodents. For the first set, we obtained an 82% of validation accuracy and 98.38% of validation accuracy for the second set using convolutional neural networks. The results were validated through cross validation and F1 metric.


Estrous cycle, GLCM, machine learning, convolutional neural network, multilayer perceptron, SVM

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