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Algorithms to process Alzheimer's disease data, based on spm12

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Neuroimgaing processing library

Requirements

  • Downloaded SPM toolbox

Processing data

The analysis pipeline can be described as following: Rearranging folders -> Processing images -> Extracting features -> Apply machine learning algorithms for classification See more details of implementation in the wiki. In order to start analysis Main_script both for Matlab and Python has to be used.

Results for current repopsitory state

AV45 images Processing results


TotalMean AgeStd AgeMMSE
MaleFemaleMaleFemaleMaleFemale
Normal40874.1372.266.085.6828.6728.96
MCI8775.275.327.165.227.5927.48
AD23775.4672.757.457.6521.2321.09

Test dataset size: 74, where: Normal: 39; MCI: 9; AD: 26

Confusion Matrix Normilized CM
NormalMCIAD
Normal3720
MCI081
AD1223
NormalMCIAD
Normal0.950.050
MCI00.880.11
AD0.0390.080.88

Best mean accuracy for 10 folds: 0.93


FDG images Processing results


TotalMean AgeStd AgeMMSE
MaleFemaleMaleFemaleMaleFemale
Normal23874.1172.045.96.0528.729.02
MCI5776.2375.246.95.5427.527.46
AD20275.572.67.637.6621.0421.06

Test dataset size: 55, where: Normal: 34; MCI: 7; AD: 14

Confusion Matrix Normilized CM
NormalMCIAD
Normal3400
MCI070
AD0014
NormalMCIAD
Normal100
MCI010
AD001

Best mean accuracy for 10 folds: 0.99

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Algorithms to process Alzheimer's disease data, based on spm12

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