-
Goal: MRI classification task using CNN (Convolutional Neural Network)
-
Code Dependency: Tensorflow 1.0, Anaconda 4.3.8, Python 2.7
-
Difficulty in learning a model from 3D medical images
- Data size is too big. e.g., 218x182x218 or 256x256x4
- There is only limited number of data. In other words, training size is too small.
- All image looks very similar and only have subtle difference between subjects.
-
Possible solutions
- Be equipped with good machine if affordable. e.g., GTX 1080 TI x 4, 16GB RAM x 4, Intel Core i7-6950X
- Downsample images in the preprocessing
- Data augmentation e.g., rotate, shift, combination
- Transfer learning
-
Notifications
You must be signed in to change notification settings - Fork 0
Study of fMRI scans of individuals with depression and individuals who have never been depressed, to detect whether a person is depressed or shows any symptoms of depression when subjected to emotional musical and non-musical stimuli
ShyamPandya/Depression_Classifier
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
Study of fMRI scans of individuals with depression and individuals who have never been depressed, to detect whether a person is depressed or shows any symptoms of depression when subjected to emotional musical and non-musical stimuli
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published