Abstract
Breast cancer is a very common disease and the cause of death of many people. It has been proven that prevention decreases the death rate, but the costs of diagnosis and image processing are very high when applied to all the population with potential risk. This paper studies an existent computer aided diagnosis method using neural network and improves its detection success rate from 60% to 73%. This improvement is achieved due to the use of image and statistical operators over concentric regions around the tumor boundaries.
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Calot, E., Merlino, H., Rancan, C., Garcia-Martinez, R. (2009). Tumor Classification on Mammographies Based on BPNN and Sobel Filter. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_4
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DOI: https://doi.org/10.1007/978-3-540-92814-0_4
Publisher Name: Springer, Berlin, Heidelberg
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