Comparison of artificial neural networks using texture parameters in the recognition of lesions in mammograms digitized
V. Andrioni, B. C. Guingo, E. L. Santana, Wagner Coelho de Albuquerque Pereira, A. F. C. Infantosi · 2011
This work proposes to use Radial Basis Function - RBF artificial neural network and Multi-Layer Perceptron MLP with the algorithm cross-validation leave-one-out, to reduce the false-positives of suspicious regions automatically detected by a difference-of-Gaussian filter in mammography. This method was applied to 175 mammograms (one real lesion/image), from the Digital Database for Screening Mammography. Was located and segmented 75.4% of lesions, with 3.55 false-positives/image. In this study, five texture parameters of real lesions and false-positive regions were extracted from a gray-level co-occurrence matrix. These parameters were input of the MLP network, trained with different backpropagation settings, and also input of the RBF network. False-positives were reduced to 1.38 per image, with 0.67 false-negatives per image. Future tests include a greater number of images to enhance the network generalization capacity.