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Health issues are common developing factor in the recent era. Brain is an important part of the body which activates the human functionalities. There are many health issues related to brain, Alzheimerdisease is one among them. If brain is dead it cannot be reversed. So, each distortion of the brain ought to be grant significance. Early diagnosis helps to reduce and avoid death rate due to Alzheimer. There are many medical diagnosis methods applied to provide medication on this issue. Computing algorithm plays a vital role in giving assistance to physicians in the diagnosis of Alzheimer disease. Objective of the work is detection ofAlzheimer disease from MRI data set. In this paper, the Alzheimer image is implemented with pre-processing techniques to reduce noise in the image which leads to the next level. In the pre-processing, filtering techniques are used to blur, and smoothen the image without changing the pixel values. Filtering techniques like Arithmetic mean, median, Gaussian, bilateral are used to remove the din in the image and Fast FourierTransform (FFT) is used to find the frequency distribution in image. Before pre-processing the image, the distribution of pixels is determined using histogram distribution for both normal and three types of Alzheimer images are Mild demented, moderate demented, very mild demented. The pixels values before pre-processing and after applying pre-processing are compared. The comparison shows the outliers removed in the types of Alzheimer image. The accuracy has proved the efficiency of the techniques used for this work.