Detection of masses on mammograms using a convolution neural network
Datong Wei, Berkman Sahiner, Heang‐Ping Chan, Nicholas Petrick · 2002
A convolution neural network (CNN) was used for classification of masses and normal tissue on mammograms. A generalized CNN was developed that uses multiple images derived from a single region of interest (ROI) as the input. The CNN input images were obtained from the ROIs using (i) averaging and subsampling; and (ii) texture feature extraction methods on smaller sub-regions inside the ROI. In (ii), features computed over different sub-regions were arranged as texture-images, and subsequently used as inputs to the CNN. The results indicate that using texture-images improves the classification accuracy.