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Various classification methods have been proposed for automatically detecting AD patients from normal patients based on features extracted from MRI and PET neuro-images. The results have achieved high accuracies up to 87% sensitivity and 95% specificity [5]–[21]. But their sensitivity in the detection of patients with Mild Cognitive Impairment (MCI) from normal patients is very lower in the range...
Recently we introduced an improved nearest neighborhood-based restoration (NNR) technique which when integrated in the second stage of the Adaptive Median Filter improved its performance in removing fixed valued impulse noise by giving an average increase of 7% in Peak Signal to Noise Ratio (PSNR) and 21% decrease in Mean Absolute Error (MAE). In our successive paper, we proposed a new adaptive center...
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