Alzheimer’s Disease Detection using Neighborhood Components Analysis and Feature Selection

In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicians in the early detection of Alzheimer’s Disease (AD) and ensure an effective diagnosis. The proposed framework is designed to be fully-automated upon the capture of the brain structure using Magnetic Resonance Imaging (MRI) scanners. The Voxel-Based Morphometry (VBM) analysis is a key element in the proposed detection process as it is intended to investigate the Gray Matter (GM) tissues in the captured MRI images. In other words, the feature extraction phase consists in encoding the voxel properties in the MRI images into numerical vectors. The resulting feature vectors are then fed into a Neighborhood Component Analysis and Feature Selection (NCFS) algorithm coupled with K-Nearest Neighbor (KNN) algorithm in order to learn a classification model able to recognize AD cases. The feature selection based on NCFS algorithm improved the overall classification performance.

@article{Ismail2020,
  title     = {Alzheimer’s Disease Detection using Neighborhood Components Analysis and Feature Selection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {10},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Maher Ben Ismail and Reema Alabdullatif and Ouiem Bchir},
  doi       = {10.14569/IJACSA.2020.0111002},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111002}
}