Alaa M. Elsayad

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The aim of this study is to apply learning vector quantization (LVQ) neural networks to classify arrhythmia from the Electrocardiogram (ECG) dataset. LVQ classification algorithms do not approximate density functions of class samples but directly define class boundaries based on prototypes, a nearest-neighbor rule and a winner-takes-it-all paradigm. It has(More)
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a(More)
Learning vector quantization (LVQ) is a feed forward neural network used for pattern classification. It has a superior performance over back propagation method in the sense of minimizing the classification errors while maintaining rapid convergence. The purpose of this study is to examine the performance of different LVQ learning algorithms on the Wisconsin(More)
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a(More)
This paper introduces an efficient algorithm for segmentation of fetal ultrasound images using the multiresolution analysis technique. The proposed algorithm decomposes the input image into a multiresolution space using the packet two-dimensional wavelet transform. The system builds features vector for each pixel that contains information about the gray(More)
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