Towards a Mobile and Fast Melanoma Detection System

DIego Rivera, Felipe Grijalva, Byron Alejandro Acuna Acurio, Robin Alvarez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

Early detection of melanoma is crucial to avoid skin cancer deaths, but only with the recent advances of deep convolutional neural networks architectures, such as MobileNet, it is possible to create a reliable enough system to detect melanoma, that can be implemented on resource constrained environments such as mobile phones or embedded systems. With this aim, this work assesses the performance of the implementation of an early melanoma recognition system using MobileNet trained from the HAM10000 database. Besides, we explain in detail two strategies to improve melanoma classification task, i.e., data augmentation on an unbalanced dataset and a multiclass approach to address a binary classification problem. Numerical results in terms of AUC metric and ROC curves corroborate the validity of our model. The performance of the proposed model is also compared to the average dermatologist performance.

Idioma originalInglés
Título de la publicación alojada2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728156668
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019 - Guayaquil, Ecuador
Duración: 11 nov. 201915 nov. 2019

Serie de la publicación

Nombre2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019

Conferencia

Conferencia6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
País/TerritorioEcuador
CiudadGuayaquil
Período11/11/1915/11/19

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