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A Neural learning algorithm for the diagnosis of breast cancer

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conference contribution
posted on 06.12.2017, 00:00 by Brijesh VermaBrijesh Verma
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 out performed other existing approaches interms 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.


Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)


Parent Title

IEEE World Congress on Computational Intelligence.

Start Date





Vancouver, Canada



Place of Publication

Piscataway, USA

Peer Reviewed


Open Access


External Author Affiliations

Faculty of Business and Informatics; TBA Research Institute;

Era Eligible


Name of Conference

International Joint Conference on Neural Networks;Congress on Evolutionary Computation;IEEE International Conference on Fuzzy Systems;IEEE World Congress on Computational Intelligence;IEEE International Conference on Neural Networks