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Cervical cancer classification using convolutional neural networks, transfer learning and data augmentation

Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos


Imágenes de la cérvix del cáncer leve(Tipo 1 y Tipo 2) y cáncer agresivo(Tipo 3).
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Cervical cancer classification using convolutional neural networks, transfer learning and data augmentation. (2021). Revista EIA, 18(35), 35008 pp. 1-12. https://doi.org/10.24050/reia.v18i35.1462

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The authors exclusively assign to the Universidad EIA, with the power to assign to third parties, all the exploitation rights that derive from the works that are accepted for publication in the Revista EIA, as well as in any product derived from it and, in in particular, those of reproduction, distribution, public communication (including interactive making available) and transformation (including adaptation, modification and, where appropriate, translation), for all types of exploitation (by way of example and not limitation : in paper, electronic, online, computer or audiovisual format, as well as in any other format, even for promotional or advertising purposes and / or for the production of derivative products), for a worldwide territorial scope and for the entire duration of the rights provided for in the current published text of the Intellectual Property Law. This assignment will be made by the authors without the right to any type of remuneration or compensation.

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Reinel Tabares Soto,

Docente e investigador de la Universidad Autonóma de Manizales

Cervical cancer is formed in the cells that line the cervix and the lower part of uterus. Due to the cost and low reasons and low supply of services for the detection of this type of cancer many women do not have access to an early an accurate diagnosis. With the purpose of solving this issue ir was created a certain method that helps us to automatically classify the different types of cervical cancer, such as mild type 1 and 2, and aggressive (type 3), using digital image processing techniques and deep learning. We have a built a computational model based on convolutional neural networks, transfer learning and data increase, which help us obtain a classification accuracy up to 97.35% on the validation data, thus, we can ensure the reliability of the results. With this work it was demonstrated that the proposed design can be used as a complement to improve the tools of the assisted diagnosis of cancer.


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