| Revista: | Latin-American Journal of Computing (LAJC) |
| Base de datos: | |
| Número de sistema: | 000565146 |
| ISSN: | 1390-9134 |
| Autores: | Stefanato, Eduardo Desteafni1 de Oliveira, Vitor Souza Premoli Pinto1 Pinheiro, Christiano Jorge Gomes2 Barroso, Regina Cely Rodrigues3 Meneses, Anderson Alvarenga de Moura4 |
| Instituciones: | 1University of São Paulo, 2Federal University of Espírito Santo, 3Rio de Janeiro State University, 4Federal University of Western Pará, |
| Año: | 2023 |
| Volumen: | 10 |
| Número: | 2 |
| Paginación: | 106-119 |
| País: | Ecuador |
| Idioma: | Inglés |
| Resumen en inglés | Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images. |
| Palabras clave: | Imágenes Biomédicas, U-Net, Segmentación Semántica, Redes Neuronales Profundas |
| Keyword: | U-Net, Semantic Segmentation, Deep Neural Networks, Biomedical Images |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
