CNN Hyperparameter Optimization for Pulmonary Nodule Classification

  • Anthony Jatobá UFAL
  • Lucas Lima USP
  • Lucas Amorim UFAL
  • Marcelo Oliveira UFAL


Redes Neurais Convolucionais (RNCs) são uma técnica poderosa para sistemas de diagnóstico auxiliado por computador, mas a configuração manual de redes complexas é inviável. A otimização automática de hiper-parâmetros é uma abordagem promissora, mas não há consenso sobre a técnica mais adequada. Neste trabalho, comparamos busca direta, probabilística e otimização bayesiana na otimização de RNCs 2D e 3D para classificação de nódulos pulmonares. Foram obtidas AUC de 0,88, sensibilidade de 87,03% e especificidade de 78,66%. Nossos experimentos demonstram o fraco desempenho da busca em grade, enquanto mostram que técnicas simples, como a busca aleatória, pode ter desempenho comparável a abordagens probabilísticas


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JATOBÁ, Anthony; LIMA, Lucas; AMORIM, Lucas; OLIVEIRA, Marcelo. CNN Hyperparameter Optimization for Pulmonary Nodule Classification. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 25-36. ISSN 2763-8952. DOI: