Detecção Automática da Depressão Assistida por Stacking DNNs em Dados de Descritores de Características Visuais

  • Filipe F. de Almeida UFMA
  • André C. B. Soares UFPI
  • Laurindo de S. B. Neto UFPI
  • Kelson R. T. Aires UFPI


Pessoas vivenciam cada vez mais sentimentos de angústia, ansiedade e tristeza. Esses apontam, entre outras patologias, à depressão e, pior, pensamentos de ideação suicida. Posto isso, técnicas computacionais capazes de apontar tal transtorno precocemente se tornam indispensáveis. O presente trabalho apresenta um modelo baseado em Stacking Deep Neural Networks para análise de expressões faciais e subsequente detecção automática da depressão. Os resultados obtidos indicam um avanço promissor quanto à detecção automática da depressão. O modelo Stacking DNNs atinge, na base de teste, 78,5% de Recall e 62,8% de F1-Score. Tais valores são 22% e 17% superiores, respectivamente, a modelos unimodais que aplicam métodos semelhantes.


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ALMEIDA, Filipe F. de; SOARES, André C. B.; B. NETO, Laurindo de S.; AIRES, Kelson R. T.. Detecção Automática da Depressão Assistida por Stacking DNNs em Dados de Descritores de Características Visuais. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 150-161. ISSN 2763-8952. DOI: