Classificando Modelos de Implantes Dentários Usando Redes Neurais Convolucionais com Dados Sintetizados - Louzada, Henrique Almeida; Paula, Maria Inês Lage de (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2021: Anais Estendidos da XXXIV Conference on Graphics, Patterns and Images, 2021)
Classifying dental implants in radiography images using Convolutional Neural Networks implies training them using images that are hardly publicly available. This work seeks to build a synthetic database of dental implants and test its effectiveness when using it to train one of these networks. Three different implant models were methodically photographed and basic Data Augmentation and Style Transfer techniques were used to create a training database. Some real X-ray images were collected to compose a test dataset and a simple Convolutional Neural Network was architected. Training this network with the synthetic set and testing it with the real set resulted in a predictive model with 71% overall accuracy, which highlights the possibility of using a synthetic database for this purpose. Implications for results and future work were discussed.