Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/53707

Título:

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

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Autor:

Giraldo-Roldán, Daniela
Nakamura, Thaís Cerqueira Reis
Claret, Anderson Faria
Santos, Giovanna Calabrese dos
Pulido-Díaz, Katya
Gerber-Mora, Roberto
Gónzalez-Pérez, Leonor Victoria
Câmara, Jeconias
Pontes, Hélder Antônio Rebelo
Martins, Manoela Domingues
Oliveira, Márcio Campos
Pereira-Prado, Vanesa
Silveira, Felipe Martins
Bologna-Molina, Ronell
Araújo, Anna Luíza Damaceno
Moraes, Matheus Cardoso
Vargas, Pablo Agustin

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Artículo

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APRENDIZAJE AUTOMÁTICO
REDES NEURONALES CONVOLUCIONALES
TUMORES ODONTOGÉNICOS
INTELIGENCIA ARTIFICIAL
APRENDIZAJE PROFUNDO

Año de publicación:

2026

Contenido:

Resumen:

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 odontogenic tumors: adenomatoid odontogenic tumor, ameloblastoma, and ameloblastic carcinoma. The goal was to investigate how transfer learning influences performance and diagnostic reliability in a clinically relevant context characterized by overlapping histological patterns. Methods An international, multicenter cross-sectional dataset of 64 hematoxylin- and eosin-stained whole slide images was analyzed, including adenomatoid odontogenic tumor (n = 16), ameloblastoma (n = 27), and ameloblastic carcinoma (n = 21). Seven CNN models (DenseNet121, EfficientNetV2B0, InceptionV3, MobileNet, ResNet50, VGG16, and Xception) were trained and tested on 455,107 patches (224 × 224 pixels). Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, F1-score, and AUC. Results Without ImageNet pretraining, DenseNet121 achieved the highest performance (accuracy = 0.73, balanced accuracy = 0.74, AUC = 0.78, specificity = 0.84, sensitivity = 0.65), followed by EfficientNetV2B0 (accuracy = 0.67, balanced accuracy = 0.68, sensitivity = 0.54). When ImageNet pretraining was applied, performance improved across all architectures. EfficientNetV2B0 reached the best overall results (accuracy = 0.79, balanced accuracy = 0.81, AUC = 0.91, specificity = 0.88, sensitivity = 0.74), while DenseNet121 maintained consistent performance (accuracy = 0.72, balanced accuracy = 0.74, AUC = 0.85, specificity = 0.84, sensitivity = 0.64). Conclusion Transfer learning with ImageNet weights enhanced the performance of most CNNs, with EfficientNetV2B0 showing the greatest responsiveness to pretraining and DenseNet121 demonstrating intrinsic robustness to initialization. These results highlight the potential of CNN-based frameworks to support the differential diagnosis of odontogenic tumors—an inherently challenging task due to morphological overlap—while establishing reproducible methodological baselines that contribute to the global development of explainable, ensemble-based, and clinically reliable AI systems in oral pathology.

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Springer

EN:

Head and Neck Pathology, 2026, 20:24.

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Citación:

Giraldo-Roldán, D, Nakamura, T, Claret, A, [y otros autores]. "Impact of transfer learning on convolutional neural networks for odontogenic tumor diagnosis". Head and Neck Pathology. [en línea] 2026, 20:24.

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1936-0568

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Licencia Creative Commons Atribución (CC - By 4.0)

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Publicaciones académicas y científicas 2020-
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