Multilayer Perceptron for squamous cell nuclei detection in Pap smear tests for cervical cancer diagnosis using local features

Alejandro Reyes Morales; Oscar Susano  Dalmau Cedeño; Teresa Efigenia Alarcón Martínez; Francisco Eduardo Oliva Ibarra

doi:10.17488/RMIB.47.1.1537

Autores/as

Alejandro Reyes Morales Universidad de Guadalajara https://orcid.org/0009-0003-1136-5259
Oscar Susano Dalmau Cedeño Centro de Investigación en Matemáticas, A.C https://orcid.org/0000-0002-1828-8458
Teresa Efigenia Alarcón Martínez Centro Universitario de los Valles, Universidad de Guadalajara https://orcid.org/0000-0002-7399-9068
Francisco Eduardo Oliva Ibarra Centro Universitario de los Valles, Universidad de Guadalajara https://orcid.org/0000-0002-1648-1924

DOI:

https://doi.org/10.17488/RMIB.47.1.1537

Palabras clave:

cervical cancer, multilayer perceptron , nuclei, squamous cell

Resumen

El cáncer cervicouterino es el cuarto tipo de cáncer más frecuentemente diagnosticado en mujeres a nivel mundial y representa una de las principales causas de muerte entre esta población. El diagnóstico se realiza manualmente a través del análisis de las células en las pruebas de Papanicolaou (Pap), lo que ocasiona retrasos en los resultados y una alta tasa de falsos positivos. Para abordar esta problemática, se propone un modelo de red neuronal artificial tipo Perceptrón Multicapa (MLP), que utiliza características locales para clasificar automáticamente los núcleos de células escamosas. El modelo fue entrenado con dos conjuntos de datos públicos: CRIC y SIPaKMeD. A diferencia de otros trabajos en el estado del arte, nuestro modelo aprende de la información de imágenes completas alcanzando una precisión de 0.9526 y 0.9397 en los conjuntos de datos CRIC y Sipakmed, respectivamente. Nuestra propuesta ofrece un enfoque sencillo mientras mantiene un rendimiento comparable al de los modelos complejos reportados en la literatura.

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