A Neural Learning Algorithm for the Diagnosis of Breast Cancer

This paper presents a new learning algorithm for the diagnosis of breast cancer. The proposed algorithm with novel network architecture can memorize training patterns with 100% retrieval accuracy as well as achieve high generalization accuracy for patterns which it has never seen before. The grey-level and BI-RADS features (radiologists´ interpretation) from digital mammograms are extracted and used to train the network with the proposed learning algorithm. The new learning algorithm has been implemented and tested on a DDSM Benchmark database. The proposed approach has outperformed other existing approaches in terms of classification rate, generalization and memorization abilities, number of iterations, fast and guaranteed training. Some promising results and a comparative analysis of obtained results are included in this paper.