| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607873 |
| ISSN: | 1405-5546 |
| Autores: | Luna Lozoya, Ricardo Salvador1 Ochoa Domínguez, Humberto de Jesús1 Sossa Azuela, Juan Humberto2 Cruz Sánchez, Vianey Guadalupe1 Vergara Villegas, Osslan Osiris1 |
| Instituciones: | 1Universidad Autónoma de Ciudad Juárez, Ciudad Juárez. México 2Instituto Politécnico Nacional, Ciudad de México. México |
| Año: | 2024 |
| Periodo: | Ene-Mar |
| Volumen: | 28 |
| Número: | 1 |
| Paginación: | 245-256 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Digital mammogram plays a key role in breast cancer screening, with microcalcifications being an important indicator of an early stage. However, these injuries are difficult to detect. In this paper, we propose a lightweight Convolutional Neural Network (CNN) for detecting microcalcifications clusters in digital mammograms. The architecture comprises two convolutional layers with 6 and 16 filters of 9×9, respectively at a full scale, a global pooling layer that eliminates the flattening and dense layers, and a sigmoid function as the output layer for binary classification. To train the model, we utilize the public INbreast database of digital mammograms with labeled microcalcification clusters. We used data augmentation techniques to artificially increase the training set. Furthermore, we present a case study that encompasses the utilization of a software application. After training, the resulting model yielded an accuracy of 99.3% with only 8,301 parameters. This represents a considerable parameter reduction as compared to the 67,797,505 used in MobileNetV2 with 99.8 % accuracy. |
| Keyword: | Microcalcifications clusters detection, Shallow convolutional neural network, Deep learning |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
