Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework

2014 ◽  
Author(s):  
Anne Luchtenberg ◽  
Rita Simões ◽  
Anne-Marie van Cappellen van Walsum ◽  
Cornelis H. Slump
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Classification Framework

Development and Evaluation of a Tactile Cognitive Function Test Device for Alzheimer’s Disease Early Detection

2015 ◽  
Vol 3 (2) ◽  
pp. 58-65 ◽  
Author(s):  
Jiajia Yang ◽  
Mohd Usairy Syafiq ◽  
Yinghua Yu ◽  
Satoshi Takahashi ◽  
Zhenxin Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Early Detection ◽  
Test Device ◽  
Function Test ◽  
Cognitive Function Test

scholarly journals Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1574
Author(s):  
Shabana Urooj ◽  
Satya P. Singh ◽  
Areej Malibari ◽  
Fadwa Alrowais ◽  
Shaeen Kalathil
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Differential Evolution ◽  
Early Stage ◽  
Selection Process ◽  
Parameter Tuning ◽  
Wavelet Neural Network ◽  
Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

Extracellular vesicles as an emerging tool for the early detection of Alzheimer’s disease

2019 ◽  
Vol 184 ◽  
pp. 111175 ◽  
Author(s):  
Tao-Ran Li ◽  
Xiao-Ni Wang ◽  
Can Sheng ◽  
Yu-Xia Li ◽  
Frederic Zhen-Tao Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Extracellular Vesicles

Probing Alzheimer's Disease Pathology and Early Detection at the NSLS with Infrared, XRF, and DEI

2008 ◽  
Vol 21 (3) ◽  
pp. 11-16 ◽  
Author(s):  
Z. Zhong ◽  
D. Bennett ◽  
D. Chapman ◽  
J. Chen ◽  
D. Connor ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection

scholarly journals TD-P-014: COGID: A SPEECH RECOGNITION TOOL FOR EARLY DETECTION OF ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_3) ◽  
pp. P191-P192
Author(s):  
Brianna Marlene Broderick ◽  
Si Long Tou ◽  
Emily Mower Provost
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Speech Recognition ◽  
Early Detection
Sign in / Sign up

Export Citation Format

Share Document