- Downloaded SPM toolbox
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.
AV45 images Processing results
Total | Mean Age | Std Age | MMSE | ||||
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Male | Female | Male | Female | Male | Female | ||
Normal | 408 | 74.13 | 72.26 | 6.08 | 5.68 | 28.67 | 28.96 |
MCI | 87 | 75.2 | 75.32 | 7.16 | 5.2 | 27.59 | 27.48 |
AD | 237 | 75.46 | 72.75 | 7.45 | 7.65 | 21.23 | 21.09 |
Test dataset size: 74, where: Normal: 39; MCI: 9; AD: 26
Confusion Matrix | Normilized CM | ||||||||||||||||||||||||||||||||
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Best mean accuracy for 10 folds: 0.93
FDG images Processing results
Total | Mean Age | Std Age | MMSE | ||||
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Male | Female | Male | Female | Male | Female | ||
Normal | 238 | 74.11 | 72.04 | 5.9 | 6.05 | 28.7 | 29.02 |
MCI | 57 | 76.23 | 75.24 | 6.9 | 5.54 | 27.5 | 27.46 |
AD | 202 | 75.5 | 72.6 | 7.63 | 7.66 | 21.04 | 21.06 |
Test dataset size: 55, where: Normal: 34; MCI: 7; AD: 14
Confusion Matrix | Normilized CM | ||||||||||||||||||||||||||||||||
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Best mean accuracy for 10 folds: 0.99