Breast cancer mammography diagnosis approach using dual tree complex wavelet transform and artificial neural networks
This paper presents a new system for breast cancer diagnosis in digital mammogram using Dual tree complex wavelet transform and artificial neural networks. This approach is based on using fractional amount of biggest coefficients, then a supervised classifier system based on Artificial neural networks is constructed to classify abnormalities. To evaluate the performance of presented system, a comparative study between Dual tree complex wavelet transform and Steerable Pyramid transform. Finally, Artificial Neural Network (ANN), the K-nearest neighbour, Support Vector Machine and Naïve Bayesian is used separately to classify the mammogram images. The proposed approach will be very valuable for breast cancer diagnosis.
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