| Journal: | Computación y Sistemas |
| Database: | PERIÓDICA |
| System number: | 000456959 |
| ISSN: | 1405-5546 |
| Authors: | Hernández Hernández, Gerardo1 Delgado Toral, Leonardo2 Ochoa Montiel, María del Rocío1 Zamora Gómez, Erik1 Sossa, Humberto1 Barreto Flores, Aldrín2 Ramos Collazo, Francisco3 Reyes Luna, Rosalina4 |
| Institutions: | 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México. México 2Benemérita Universidad Autónoma de Puebla, Facultad en Ciencias de la Electrónica, Puebla. México 3Benemérita Universidad Autónoma de Puebla, Bioterio Claude Bernard, Puebla. México 4Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias Biológicas, Puebla. México 5Instituto Tecnológico y de Estudios Superiores de Monterrey, Guadalajara, Jalisco. México |
| Year: | 2019 |
| Season: | Oct-Dic |
| Volumen: | 23 |
| Number: | 4 |
| Pages: | 1249-1259 |
| Country: | México |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Aplicado, descriptivo |
| English 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 the 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 built to solve the main problems, the research of the reproductive cycle and the reproduction control of rodents. For the first set, we obtained 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 |
| Disciplines: | Ciencias de la computación |
| Keyword: | Inteligencia artificial, Procesamiento de datos, Programación, Redes, Ciclo estral, Matriz de niveles de grises de coocurrencia, Aprendizaje automático, Redes neuronales convolucionales, Perceptrón multicapa, Máquinas de vectores de soporte |
| Keyword: | Artificial intelligence, Data processing, Programming, Networks, Estrous cycle, Co-occurrence grey level matrix, Machine learning, Convolutional neural network, Multilayer perceptron, Support vector machines |
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