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A Computer Aided Diagnosis System for Identifying Alzheimer’s from MRI Scan using Improved Adaboost

  • S. SaravanakumarEmail author
  • P. Thangaraj
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

The recent studies in Morphometric Magnetic Resonance Imaging (MRI) have investigated the abnormalities in the brain volume that have been associated diagnosing of the Alzheimer’s Disease (AD) by making use of the Voxel-Based Morphometry (VBM). The system permits the evaluation of the volumes of grey matter in subjects such as the AD or the conditions related to it and are compared in an automated manner with the healthy controls in the entire brain. The article also reviews the findings of the VBM that are related to various stages of the AD and also its prodrome known as the Mild Cognitive Impairment (MCI). For this work, the Ada Boost classifier has been proposed to be a good selector of feature that brings down the classification error’s upper bound. A Principal Component Analysis (PCA) had been employed for the dimensionality reduction and for improving efficiency. The PCA is a powerful, as well as a reliable, tool in data analysis. Calculating fitness scores will be an independent process. For this reason, the Genetic Algorithm (GA) along with a greedy search may be computed easily along with some high-performance systems of computing. The primary goal of this work was to identify better collections or permutations of the classifiers that are weak to build stronger ones. The results of the experiment prove that the GAs is one more alternative technique used for boosting the permutation of weak classifiers identified in Ada Boost which can produce some better solutions compared to the classical Ada Boost.

Keywords

Magnetic Resonance Imaging (MRI) Alzheimer’s Disease (AD) Voxel-Based Morphometry (VBM) Principal Component Analysis (PCA) Genetic Algorithms (GA) and Greedy Search 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Research ScholarAnna UniversityChennaiIndia
  2. 2.Department of Computer Science and EngineeringBannari Amman Institute of TechnologySathyamangalamIndia

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