A Modular Neural Network System for the Analysis of Nuclei in Histopathological Sections
Constantinos S. Pattichis, F. Schnorrenberg, Christos N. Schizas, Marios S. Pattichis, Kyriacos C. Kyriacou · Studies in fuzziness and soft computing · 2002
The evaluation of immunocytochemically stained histopathological sections presents a complex problem due to many variations that are inherent in the methodology. This chapter describes a modular neural network system which is being used for the detection and classification of breast cancer nuclei named Biopsy Analysis Support System (BASS). The system is based on a modular architecture where the detection and classification stages are independent. Two different methods for the detection of nuclei are being used: the one approach is based on a feed forward neural network (FNN) which uses a block-based singular value decomposition (SVD) of the image, to signal the likelihood of occurrence of nuclei. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.