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Mammographic Mass Detection using Wavelets as Input to Neural Networks

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Abstract

The objective of this paper is to demonstrate the utility of artificial neural networks, in combination with wavelet transforms for the detection of mammogram masses as malign or benign. A total of 45 patients who had breast masses in their mammography were enrolled in the study. The neural network was trained on the wavelet based feature vectors extracted from the mammogram masses for both benign and malign data. Therefore, in this study, Multilayer ANN was trained with the Backpropagation, Conjugate Gradient and Levenberg–Marquardt algorithms and ten-fold cross validation procedure was used. A satisfying sensitivity percentage of 89.2% was achieved with Levenberg–Marquardt algorithm. Since, this algorithm combines the best features of the Gauss–Newton technique and the other steepest-descent algorithms and thus it reaches desired results very fast.

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Authors and Affiliations

  1. Electrical&Electronics Department, The Faculty of Engineering, Istanbul University, 34320, Avcilar, Istanbul, Turkey

    Niyazi Kilic & Osman N. Ucan

  2. Computer Science Department, The Faculty of Engineering, Istanbul University, 34320, Avcilar, Istanbul, Turkey

    Pelin Gorgel & Ahmet Sertbas

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  1. Niyazi Kilic
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  2. Pelin Gorgel
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  4. Ahmet Sertbas
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Correspondence to Niyazi Kilic.

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Kilic, N., Gorgel, P., Ucan, O.N. et al. Mammographic Mass Detection using Wavelets as Input to Neural Networks. J Med Syst 34, 1083–1088 (2010). https://doi.org/10.1007/s10916-009-9326-1

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  • DOI: https://doi.org/10.1007/s10916-009-9326-1

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