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

DOI: https://doi.org/10.5753/sbcas.2023.229573

Resumo

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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