TY - GEN
T1 - Classification of the estrous cycle through texture and shape features
AU - Delgado, Leonardo
AU - Hernandez, Gerardo
AU - Zamora, Erik
AU - Sossa, Humberto
AU - Barreto, Aldrin
AU - Ramos, Francisco
AU - Reyes, Rosalina
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - We show, 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 used texture and shape features on the gray level color space and CIELAB color space on channels A and B, which were classified using support vector machines (SVM) and the artificial neural network multilayer perceptron (MLP). As 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 Estrous 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 87% of validation accuracy and 100% of validation accuracy for the second set using the multilayer perceptron. The results were validated through cross validation using 5 sets and F1 metric.
AB - We show, 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 used texture and shape features on the gray level color space and CIELAB color space on channels A and B, which were classified using support vector machines (SVM) and the artificial neural network multilayer perceptron (MLP). As 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 Estrous 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 87% of validation accuracy and 100% of validation accuracy for the second set using the multilayer perceptron. The results were validated through cross validation using 5 sets and F1 metric.
KW - Estrous cycle
KW - GLCM
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85046133792&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8285390
DO - 10.1109/SSCI.2017.8285390
M3 - Contribución a la conferencia
AN - SCOPUS:85046133792
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -