Combined Classifiers for Detection of Breast Cancer Metastasis in Histopathological Images

• Luis Jhonne Carvalhal de Melo UEMA
• Omar Andres Carmona Cortes IFMA
• Antônio Fernando Lavareda Jacob Júnior UEMA

DOI: https://doi.org/10.5753/eniac.2025.14404

Resumo

O câncer de mama é uma doença responsável pela maioria das mortes no Brasil e afeta principalmente mulheres. Entre as consequências de sua ocorrência estão a predisposição genética, o sedentarismo e a menopausa tardia. É uma doença que requer diagnóstico o mais rápido possível para que o paciente possa iniciar o tratamento. Além disso, estudos sugerem que patologistas podem atingir uma precisão de cerca de 72% ao analisar um exame composto por milhares de imagens histopatológicas de cortes de linfonodos. Nesse contexto, este trabalho descreve um classificador que combina ResNet50, Random Forest (RF) e Support Vector Machine (SVM) treinados pelo conjunto de dados PatchCamelyon. Os resultados indicam que o método proposto atingiu uma precisão e F1-score de 81%.

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