Estrous Cycle Classification through Automatic Feature Extraction

Authors

  • Gerardo Hernández Hernández Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Leonardo Delgado Toral Benemérita Universidad Autónoma de Puebla, Facultad en Ciencias de la Electrónica
  • María del Rocío Ochoa Montiel Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Erik Zamora Gómez Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Juan Humberto Sossa Azuela Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Aldrín Barreto Flores Benemérita Universidad Autónoma de Puebla, Facultad en Ciencias de la Electrónica
  • Francisco Ramos Collazo Benemérita Universidad Autónoma de Puebla, Bioterio Claude Bernard
  • Rosalina María de Lourdes Reyes Luna Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias Biológicas

DOI:

https://doi.org/10.13053/cys-23-4-3095

Keywords:

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

Abstract

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.

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Published

2019-12-20

Issue

Vol. 23 No. 4 (2019): 23-4(2019)

Section

Articles

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