# Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection

## TL;DR

This paper introduces a machine learning framework for analyzing brain MRIs to detect Alzheimer's disease by improving image quality and using various classifiers.

## Contribution

The novel contribution is an evolutionary framework combining image processing and multiple machine learning classifiers for Alzheimer's detection.

## Key findings

- The framework uses adaptive mean filtering and histogram equalization to enhance MRI image quality.
- LS-SVM-RBF, SVM, KNN, and random forest classifiers are compared for Alzheimer's detection accuracy.
- Performance metrics like accuracy, sensitivity, and specificity are evaluated for each classifier.

## Abstract

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.