Ahmad Taha Abdulsadda

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Two-dimensional (2D) autoregressive moving average (ARMA) random fields have been proven to be accurate models of ultrasound breast images. However, the stability properties of these models have not been examined. In this paper, we investigate the stability of 2D ARMA models in ultrasound breast images, and use the estimated 2D ARMA coefficients as a basis(More)
— this paper presents a Wavelet-based Recurrent Fuzzy Neural Networks (WRFNN) trained with a stochastic search-based adaptation algorithm. A WRFNN represents a recurrent network of neurons employing wavelet functions whose outputs are combined using fuzzy rules. In this paper an earlier WRFNN model proposed by Lin, and Chin, [1], is modified by application(More)
The key to surgical planning for breast conservation is tumor localization. An accurate localization of the breast tumor is essential to guide the surgeon to the lesion, and ensure its correct and adequate removal with satisfactory excision margins. Current breast tumor localization techniques are invasive and often result in a cosmetic disfigurement. In(More)
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