NUMERO: #20230981

Título: DEEP LEARNING FOR SALIVARY GLAND TUMOR CLASSIFICATION

Nome do Apresentador: Anna Luiza Damaceno ARAUJO

Categoria do Trabalho: Painel de pesquisa científica (PPC)

Área Temática: Estomatologia

Resumo: Objective: The present study proposes to develop and implement a Deep Learning model for automatic classification of clinical photographs of salivary gland tumors in the palate into benign and malignant categories.
Study Design: A dataset of 100 clinical images of SGT from seven institutions was used to train and validate a ResNet50 (original architecture) implemented with a low learning rate of 10-5 for 75 epochs with 10-fold cross validation.
Results: The proposed ResNet50 reached an accuracy of 70% and AUC of 0.68 during training, showing the potential of learning. However, divergency on training and validation accuracy and loss curves displayed a clear overfitting, which is not uncommon when training Deep Learning algorithms with a small sample.
Conclusion: The proposed DL-based model presented a capacity for learning with the potential of achieving a fair accuracy. To overcome overfitting and improve the results, further steps of the present investigation will consider transfer learning and data augmentation.

Autor 1:  Anna Luiza Damaceno ARAUJO

E-mail 1:  [email protected]

Autor 2:   Viviane Mariano DA SILVA

E-mail 2:  [email protected]

Autor 3 :  Daniela Giraldo ROLDÁN

E-mail 3:  [email protected]

Autor 4:  Márcio Ajudarte LOPES

E-mail 4:  [email protected]

Autor 5:  Matheus Cardoso MORAES

E-mail 5:  [email protected]

Autor 6:  Luiz Paulo KOWALSKI

E-mail 6:  [email protected]

Autor 7:  Alan Roger SANTOS-SILVA

E-mail 7:  [email protected]