Classificação de Tumores Cerebrais em Imagens de Ressonância Magnética Utilizando Técnicas de Aprendizado de Máquina
DOI:
https://doi.org/10.5753/reic.2026.7814Keywords:
Tumores cerebrais, Ressonância magnética, Aprendizado de máquina, Random Forest, Classificação de imagens médicasAbstract
Este estudo investigou a aplicação de técnicas de aprendizado de máquina na classificação automática de tumores cerebrais em imagens de ressonância magnética, visando apoiar o diagnóstico médico. As imagens passaram por etapas de pré-processamento, incluindo aumento de dados (rotações, espelhamentos e ajustes de brilho), extração e normalização de características, além de redução de dimensionalidade para preservar apenas as informações mais relevantes. Foram avaliados e comparados os algoritmos Random Forest e Support Vector Machine (SVM) em diferentes cenários experimentais. O Random Forest apresentou o melhor desempenho, alcançando aproximadamente 98% de acurácia e menor taxa de falsos negativos, aspecto fundamental em aplicações clínicas. O SVM também obteve resultados satisfatórios, com acurácia em torno de 94%, embora tenha demonstrado maior sensibilidade às variações dos dados. Os resultados evidenciam o potencial do aprendizado de máquina como ferramenta de apoio à análise de imagens médicas e ao diagnóstico de tumores cerebrais.
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