Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC Maps

TL;DR

This study explores using multilayer perceptrons and Kohonen SOM classifiers to analyze diffusion-weighted MR images for early Alzheimer's diagnosis, offering an alternative to traditional ADC maps.

Contribution

It introduces a novel classification approach combining neural networks and SOMs to evaluate cerebrospinal fluid and disease progression in MR images.

Findings

Classification accuracy improved over traditional ADC analysis

Neural network methods effectively separated disease stages

Proposed approach offers a non-invasive diagnostic alternative

Abstract

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on multilayer perceptrons and Kohonen Self-Organized Map classifiers. We assume the classes of interest can be separated by hyperquadrics. Therefore, a 2-degree polynomial network is used to classify the original image, generating the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.