1. Moving from Detection to Pre-detection of Alzheimer’s Disease from MRI Data
K A N N P Gunawardena

2. Alzheimer’s Disease

3. Alzheimer’s Disease Mostly affects at the age of 60 & older.
A progressive disease.
Leads to nerve cell death and tissue loss throughout the brain.
MMSE and PAL.
No permanent cure.
Pre-detection of AD is seen as important.
Most common type of dementia.
Almost 70% of dementia cases are AD.
Symptoms develop slowly, getting worse over time.
But treatments for symptoms. The treatments cannot stop AD progression but they can temporarily slow the worsening,
Because treatment may be most efficacious if introduced as early as possible.

4. Alzheimer’s Disease
More recently, there has been an understanding that MRI may add positive predictive value to a diagnosis of Alzheimer’s disease.
Yellow - cortex shrivels up
Purple - hippocampus get shrink
Blue - Ventricles grow larger

5. Research Questions

6. Research Questions
How feasible is it to implement a pre-detection system to classify different stages of Alzheimer’s disease using previous approaches such as Support Vector Machines (SVM)?

7. Detection and Pre-detection

8. Detection and Pre-detection
Why detection before pre-detection?
Most studies have focused on detection and have achieved successful results
Disease-modifying drugs will be most effective if administered early in the course of disease,
Can find the best sources of support.
Can make decisions about the future.
Therefore Pre-detection is highly important.

9. Initial experiment

10. Initial experiment Design
Verification of existing automated identification methods
If it fails propose a new technique for this purpose

11. Alzheimer's subjects (46)
Initial experiment
Used MRI data available at ADNI.
Sample group (69)
Alzheimer's subjects (46)
Control subjects (23)

12. Initial experiment Convert into series of DICOM images.
Detect the cerebral ventricle volume
Apply Image processing techniques.

13. Initial experiment
Support Vector Machines (SVM) with linear kernels for classification
Result
Sensitivity – 86.67%
Specificity – 98.54%
Misclassifications

14. Next Step

15. Next Step
What is CNN ?
Why CNN ?

16. Summary

17. Summary Alzheimer’s Disease. Research Questions.
The design of this study is twofold.
Used MRI data made available as part of the AD Neuroimaging Initiative.
In the initial experiment SVM with linear kernel is used for classification.
14.59% were misclassified in initial experiment.
Why CNN is good for this purpose.