A Review of Image-Based Deep Learning Algorithms for Cervical Cancer Screening

Franco Tasso Parraga, Ciro Rodriguez, Yuri Pomachagua, DIego Rodriguez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The significant advance in artificial intelligence has posed many challenges, with disease detection being one of the most important. Early detection can be very important in preventing progressive disease progression and can help provide accurate treatment options. Cervical cancer is the fourth type of cancer most common in women. In 2018, 570 000 cases were estimated in women around the world. This article aims to present a review of different image-based algorithms for cervical cancer screening. For the research process, three important sources of information were considered: Scopus, Web of Science, and PubMed, considering a total of 12 articles taking into account the last five years. The articles were analyzed considering the databases used, the preprocessing of the images, the segmentation of the images, the classification of images, and the proposals' results. The results show great advances in the techniques used for cervical cancer screening, with convolutional neural networks being the most widely used technique. In addition, including the segmentation stage in the construction of the models can significantly increase precision. Finally, it is shown that the k-fold cross validation technique is one of the most used and efficient techniques to validate the models.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas155-160
Número de páginas6
ISBN (versión digital)9781728176956
DOI
EstadoPublicada - 22 set. 2021
Evento13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Perú
Duración: 22 set. 202123 set. 2021

Serie de la publicación

NombreProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

Conferencia

Conferencia13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
País/TerritorioPerú
CiudadLima
Período22/09/2123/09/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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