Classi cation Results of Arti cial Neural Networks for Alzheimer's Disease Detection

33 Pages·2009·0.77 MB·English

Classi(cid:28)cation Results of Arti(cid:28)cial Neural Networks for
Alzheimer’s Disease Detection
Alexandre Savio1, Maite Garc(cid:237)a-SebastiÆn1,
Carmen HernÆndez1, Manuel Graæa1, Jorge Villanœa2
1ComputationalIntelligenceGroup,UniversityoftheBasqueCountry
2Osatek,HospitalDonostia
10th International Conference on Intelligent Data Engineering and
Automated Learning
(IDEAL 2009)
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa1te/k,3H3ospitalDonostia)
Outline
1 Introduction
Alzheimer’s Disease
Motivation
Introduction to the Analysis Methods
Materials and Methods
2 Results
3 Conclusions and Further Work
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa2te/k,3H3ospitalDonostia)
Introduction Alzheimer’sDisease
Outline
1 Introduction
Alzheimer’s Disease
Motivation
Introduction to the Analysis Methods
Materials and Methods
2 Results
3 Conclusions and Further Work
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa3te/k,3H3ospitalDonostia)
Introduction Alzheimer’sDisease
Alzheimer’s Disease (AD)
Neurodegenerative disorder and one of the most common cause of
dementia in old people.
Still incurable and terminal.
Although noninvasive approaches for antemortem diagnosis of AD are
under development, de(cid:28)nitive diagnosis requires a postmortem study
of the brain tissue.
T1 weighted MRI scans (sMRI) promises to aid diagnosis and
treatment monitoring of AD, o(cid:27)ering the potential for easily obtainable
surrogate markers of diagnostic status and disease progression.
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa4te/k,3H3ospitalDonostia)
Introduction Motivation
Outline
1 Introduction
Alzheimer’s Disease
Motivation
Introduction to the Analysis Methods
Materials and Methods
2 Results
3 Conclusions and Further Work
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa5te/k,3H3ospitalDonostia)
Introduction Motivation
Objective
Objective
Detection of patients with very mild to mild Alzheimer’s disease.
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa6te/k,3H3ospitalDonostia)
Introduction Motivation
Our approach
Using sMRI and standard classi(cid:28)ers:
Feature extraction based on Voxel-based Morphometry (VBM) analysis
Backpropagation (BP)
Radial Basis Function Networks (RBFN)
Learning Vector Quantization Networks (LVQ)
Probabilistic Neural Network (PNN)
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa7te/k,3H3ospitalDonostia)
Introduction Motivation
Di(cid:27)erential features of our work
This issue has been addressed in many other works.
The di(cid:27)erences here are:
Freely available database with good quality images and
well-documented.
The number of subjects selected for this study is relatively high.
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa8te/k,3H3ospitalDonostia)
Introduction IntroductiontotheAnalysisMethods
Outline
1 Introduction
Alzheimer’s Disease
Motivation
Introduction to the Analysis Methods
Materials and Methods
2 Results
3 Conclusions and Further Work
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasqIuDeECAoLun09try,Osa9te/k,3H3ospitalDonostia)
Introduction IntroductiontotheAnalysisMethods
Voxel-based Morphometry (VBM)
Morphometry analyses allow a measurement of structural di(cid:27)erences
within or across groups throughout the entire brain.
VBM measures di(cid:27)erences in local concentrations of brain tissue,
through a voxel-wise comparison of multiple brain images.
The most popular brain morphometry analysis.
AlexandreSavio,MaiteGarc(cid:237)a-SebastiÆn (ComputAatNioNnsalvIsn.teAllDige(nrecseuGltsr)oup,UniversityoftheBasIqDuEeACLou0n9try,Os1a0te/k,3H3ospitalDonostia)