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This paper shows the application of a genetic algorithm (GA) for component selection to improve the accuracy of a kNN (k-Nearest Neighbor) method when using it for breast cancer prognosis. Our GA uses the best chromosome (member) in a generation to produce new ones. The probabilities of crossover and mutation have not fixed values, instead they depend on the evaluation of the chromosomes involved...
This paper shows a new way of applying both a spatial filter and the discrete wavelet transform to reduce the noise in X-ray medical images. Our method combines a Gaussian filter and a Meyer wavelet transform. We experimentally found that it helps to improve the PSNR of an image from 0.5 dB to almost 1.0 dB when compared to other known methods.
This work shows a detailed description of the kNN method when using it for breast cancer diagnosis. We show the relation of the number of neighbors to the accuracy and also the change of accuracy with the change of the percentage of the data used for diagnosis. We also show details about the variation of the maximum and minimum values of the accuracy with the percentage of data used for diagnosis...
Breast cancer is the world's second most frequent type of cancer and in Japan it is the third most frequent one. The prognosis of its recurrence, after a first treatment, is very important to increase the survival rate of a patient. This work shows the application of the k-Nearest Neighbors (kNN) method to prognosis breast cancer and also proposes a method to select a good setting with the parameters...
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