Palavras-chave
Redes Neurais Convolucionais
Ressonância Magnética
Como Citar
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Resumo
Objetivo: Propor um algoritmo inteligente para diagnosticar a Doença de Alzheimer usando exames de ressonância magnética, além de comparar o desempenho de cinco modelos de redes neurais convolucionais (ResNet50, VGG19, DenseNet201, InceptionV3 e EfficientNetB7) na classificação automática de estágios da doença. Métodos: Nesta pesquisa, foi feio um estudo experimental realizado com 416 exames de ressonância magnética funcional oriundos do dataset OASIS-1, envolvendo pacientes entre 18 e 96 anos. Foram utilizadas imagens segmentadas entre as fatias 100 e 160, com pré-processamento padronizado e treinamento dos modelos utilizando o otimizador Adam (taxa de aprendizado 10⁻³, batch size 32), com parada antecipada e redução adaptativa da taxa de aprendizado. Resultados: Na classificação multiclasses (Sem Demência, Demência ?Muito Leve, Demência Leve e Demência Moderada), ResNet50 obteve acurácia de 83%, DenseNet201 de 82% e VGG19 de 80%, destacando-se frente ao InceptionV3 (78%) e EfficientNetB7 (72%). A abordagem hierárquica binária (com demência e sem demência) demonstrou ganhos em precisão, com destaque para a ResNet50, que atingiu acurácia de 96%. Conclusão: As redes convolucionais apresentam desempenho promissor para o diagnóstico assistido da Doença de Alzheimer, especialmente com a adoção de estratégias hierárquicas que reduzem erros na distinção entre os estágios da doença.
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