| Resumen |
We introduce a residual shallow Convolutional Neural Network (CNN) designed for classifying the presence or absence of Microcalcification Clusters (MCCs) in digital mammograms. MCCs are the most indirect signs of breast cancer in the early stages, and when they are detected on time, the women have a 5-year survival rate of 99%. However, MCCs are difficult to detect. The proposed architecture comprises three Convolutional Layers (CLs) with 6, 16, and 16 filters, each of size 9 × 9 at full scale, including a residual block. Subsequently, a Global Max Pooling operator transforms the three-dimensional input tensor into a 16-valued vector, avoiding flattening and dense layers. A sigmoid function at the output layer allows binary classification. The CNN was evaluated using the public INbreast database. We augmented the data through four geometric transformations to enhance the CNN accuracy. We show that the residual shallow CNN achieves an accuracy of 99.71% with 29,053 parameters, over 2,333 times smaller than the MobileNetV2, which attains 99.8% accuracy but with 67,797,505 parameters. Additionally, our proposed CNN was subjected to a systematic ablation study, based on the CLs energy, to observe the importance of the filters and reduce the number of parameters after training. © 2024 Elsevier Ltd |
