Evaluation of Melanoma Diagnosis using Imbalanced Learning

  • Lucas Bezerra Maia
  • Alan Carlos Lima
  • Pedro Thiago Cutrim Santos
  • Nigel da Silva Lima
  • João Dallyson Sousa de Almeida
  • Anselmo Cardoso Paiva

Resumo


Melanoma is the most lethal type of skin cancer when compared to others, but patients have high recovery rates if the disease is discovered in its early stages. Several approaches to automatic detection and diagnosis have been explored by different authors. Training models with the existing data sets has been a difficult task due to the problem of imbalanced data. This work aims to evaluate the performance of machine learning algorithms combined with imbalanced learning techniques, regarding the task of melanoma diagnosis. Preliminary results have shown that features extracted with ResNet Convolutional Neural Network, along with Random Forest, achieved an improvement of sensibility of approximately 21%, after balancing the training data with Synthetic Minority Oversampling TEchnique (SMOTE) and Edited Nearest Neighbor (ENN) rule.

Publicado
26/07/2018
MAIA, Lucas Bezerra; LIMA, Alan Carlos; SANTOS, Pedro Thiago Cutrim; LIMA, Nigel da Silva; DE ALMEIDA, João Dallyson Sousa; PAIVA, Anselmo Cardoso. Evaluation of Melanoma Diagnosis using Imbalanced Learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2018.3680.