Comparative Evaluation of Two Neural Network Based Techniques for the Classification of Microcalcifications in Digital Mammograms

This paper investigates two neural network based techniques for the classification of microcalcifications in digital mammograms. Both techniques extract suspicious areas containing microcalcifications from digital mammograms and classify them into two categories: whether they contain benign or malignant clusters. The centroids and radii provided by expert radiologists are being used to locate and extract suspicious areas. Two neural networks based on iterative and non-iterative training methods are used to classify them into benign or malignant. The proposed techniques have been implemented in C++ on the SP2 supercomputer. The database from the Department of Radiology at the University of Nijmegen has been used for the experiments. The comparative results are very interesting and promising. Some of them are included in this paper.