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Automatic Detection of ADHD subjects using Deep Convolutional Neural Network Arjun Watane , Soumyabrata Dey ([email protected], [email protected]) University of Central Florida. III. Formulation:. Slices. Problem & Motivation: Automatic detection of ADHD Structural MRI - PowerPoint PPT Presentation
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Automatic Detection of ADHD subjects using Deep Convolutional Neural Network
Arjun Watane, Soumyabrata Dey([email protected], [email protected])
University of Central Florida
I. Problem & Motivation:
Automatic detection of ADHDStructural MRI
Strict 3-D anatomical structure
Lack of biological measures for diagnosis Subjective to Verbal Test Inconsistent and over-diagnosis problem
Data set: NYU data center of ADHD-200 203 training, 41 test subjects
Network Configurations
Input Blob – 203 Subjects, 21 slices per subject, 256x256 pixels slice image
Layer – convolution and max-pooling to generate feature map
5 convolution layers, 4 max pooling layers, 2 fully connected layers (FC)
Extraction of features using pretrained Imagenet model
III. Formulation: Slice 1
Slice 2
Slice n
Combine FC6 and FC7 for each slice
Vector Length = 4096x2 = 8192
SVM Classifier
Slice 1 Decision
Convolutional Neural Network
Extraction of Feature Layer FC6 and FC7
Slice 2 Decision
Slice n Decision
Slices
Type SliceAccuracyFC6 %
Accuracy FC7 %
Accuracy FC6 and FC7 %
1 GM 75 65.85 63.42 78.05
2 Normal 55 70.73 51.22 78.05
3 WM 55 46.34 60.98 78.05
4 NGW 195 63.41 65.85 70.73
5GM (Late
Fusion) 75, 85, 115 75.61 78.05 80.49
Late Fusion of FC6 and FC7 features showed the highest accuracy of ADHD classification, at 80.49%.
V. Visualization of Features :
Gray Matter
White Matter
Normalized
Slices
1 2 3 4 50
10
20
30
40
50
60
70
80
90
IV. Image Pre-Processing :
VI. Results :
Accuracy Comparison of Independent Feature vs. Feature Combination
Brain Segmentation
Convolution 1 Convolution 2 Convolution 3 Convolution 4 Convolution 5
FC6+FC7
FC7
FC6
Weighted Late Fusion
DW .TfinalD
:finalD Final decision
:W Weight vector
:D Decision vector
,2
,2
1
)2(
2
ni
ni
ie
e
S
02 ni
02 ni
''2
'1
' ,...,, nwwwW : Calculated from training data
,'iii wSw
2ni 0
1
II. Convolutional Neural Network :
FC6 FC72048 2048
nwww ,...,, 21W
},...,2,1{ ni
Slices are ranked based on the score. Slice1 has highest weight'iw