  Alzheimer's Disease (AD) is the most common type of dementia. In all leading
countries, it is one of the primary reasons of death in senior citizens.
Currently, it is diagnosed by calculating the MSME score and by the manual
study of MRI Scan. Also, different machine learning methods are utilized for
automatic diagnosis but existing has some limitations in terms of accuracy. In
this paper, we have proposed some novel preprocessing techniques that have
significantly increased the accuracy and at the same time decreased the
training time of various classification algorithms. First, we have converted
the ADNI dataset which was in 4D format into 2D form. We have also mitigated
the computation costs by reducing the parameters of the input dataset while
preserving important and relevant data. We have achieved this by using
different preprocessing steps like grayscale image conversion, Histogram
equalization and selective clipping of dataset. We observed a highest accuracy
of 97.52% and a sensitivity of 97.6% in our testing dataset.
