Explorando Arquiteturas de Redes Neurais Profundas na Classificação de Imagens de Cariótipos Humanos

• Francisco das C. Imperes Filho UFPI
• Vinicius P. Machado UFPI
• Semiramis J. H. do Monte UFPI
• Alan R. F. dos Santos UFPI
• Leonardo P. de Sousa UFPI
• Adalberto S. da Silva UFPI
• Ester M. Pereira UFPI
• Rodrigo de M. S. Veras UFPI

DOI: https://doi.org/10.5753/sbcas.2024.2798

Resumo

A análise cromossômica, uma prática clinicamente crucial realizada tradicionalmente por geneticistas, pode ser suscetível à fadiga ao longo do tempo, afetando a qualidade dos diagnósticos. Neste artigo, exploramos a classificação automatizada de imagens de cromossomos por meio de diversas arquiteturas de redes neurais profundas. Avaliamos 23 pares de cromossomos humanos em uma tarefa multiclasse, revelando resultados promissores. Destacam-se os desempenhos superiores da arquitetura DenseNet169, alcançando uma acurácia, precisão, recall e F1-Score de 98,77%. O índice de concordância Kappa atingiu um nível ”Excelente”(0,99), enquanto um baixo desvio padrão (0,002) ressaltou a consistência das métricas, conferindo confiabilidade e previsibilidade ao modelo proposto.

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