Enhancing Learnability of classification algorithms using simple data preprocessing in fMRI scans of Alzheimer's disease

TL;DR

This paper introduces novel preprocessing techniques for fMRI data that significantly improve the accuracy and efficiency of classification algorithms in diagnosing Alzheimer's disease.

Contribution

It proposes specific preprocessing methods like dataset conversion, grayscale transformation, and histogram equalization to enhance machine learning diagnosis of AD.

Findings

Achieved up to 97.52% accuracy in AD classification

Reduced training time through dataset parameter reduction

Improved sensitivity to 97.6% in testing

Abstract

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.