Impact of transfer learning on convolutional neural networks for odontogenic tumor diagnosis.

Objective This study aimed to evaluate the coherence between data heterogeneity and model complexity by comparing seven convolutional neural network (CNN) architectures—trained with and without ImageNet pretraining—in a multiclass framework for the histopathological classification of three odontogen...

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Bibliografiske detaljer

Hovedforfatter:	Giraldo-Roldán, Daniela (author)
Andre forfattere:	Nakamura, Thaís Cerqueira Reis (author), Claret, Anderson Faria (author), Santos, Giovanna Calabrese dos (author), Pulido-Díaz, Katya (author), Gerber-Mora, Roberto (author), Gónzalez-Pérez, Leonor Victoria (author), Câmara, Jeconias (author), Pontes, Hélder Antônio Rebelo (author), Martins, Manoela Domingues (author), Oliveira, Márcio Campos (author), Pereira-Prado, Vanesa (author), Silveira, Felipe Martins (author), Bologna-Molina, Ronell (author), Araújo, Anna Luíza Damaceno (author), Moraes, Matheus Cardoso (author), Vargas, Pablo Agustin (author)
Format:	article
Sprog:	engelsk
Udgivet:	2026
Fag:	APRENDIZAJE AUTOMÁTICO REDES NEURONALES CONVOLUCIONALES TUMORES ODONTOGÉNICOS INTELIGENCIA ARTIFICIAL APRENDIZAJE PROFUNDO
Online adgang:	https://hdl.handle.net/20.500.12008/53707
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