Classification of Mammograms Using Sigmoidal Transformation and SVM

Author:

Bhateja Vikrant,Tiwari Ananya,Gautam Aman

Publisher

Springer Singapore

Reference17 articles.

1. Jain, A., Singh, S., Bhateja, V.: A Robust Approach for Denoising and Enhancement of Mammographic Breast Masses, In: International Journal on Convergence Computing, vol. 1, no. 1, pp. 38–49, Inderscience Publishers (2013).

2. Bhateja, V., Urooj, S., Misra, M.: Technical Advancements to Mobile Mammography using Non-Linear Polynomial Filters and IEEE 21451-1 Information Model, In: IEEE Sensors Journal, vol. 15, no. 5, pp. 2559–2566, Advancing Standards for Smart Transducer Interfaces (2015).

3. Tang, J., Liu, X., Sun, Q.: A Direct Image Contrast Enhancement Algorithm in the Wavelet Domain for Screening Mammograms, In: IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 1, pp. 74–80, IEEE (2009).

4. Aarthi, R., Divya, K., Komala, N., Kavitha, S.: Application of Feature Extraction and Clustering in Mammogram Classification using Support Vector Machine, In: International Conference on Advanced Computing (ICoAC), pp. 62–67, IEEE (2011).

5. Subashini, T. S., Ramalingam, V., Palanivel, S.: Automated assessment of breast tissue density in digital mammograms, In: Computer Vision and Image Understanding, vol. 114, no. 1, pp. 33–43, Elsevier (2010).

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