CNN

2. N
.47 6. .4 5 1. 32 3187 32
D
C
S

3. [1] https://www.irasutoya.com/p/figure.htm
[1]
[1]
•
•
•
• CT
• MRI
•
•

4. 10
Ø
Ø

5. 4273
1583
D. Kermany, K. Zhang, M. Goldbaum
: https://data.mendeley.com/datasets/rscbjbr9sj/2

6. Neural Network
Neural Network
CNN
Convolution layer Pooling layer
Kernel
Input layer
Output layer
Hidden layer

7. Depthwise Separable Convolution layer
K
K
F
F
N M
F
F
F:
K:
N,M:
N
!"#×%"×& !"#×&
Convolution layer
K
K
1
N
F
F
1
1
N
MN
'(×)(×* + *×)(×, !"# + #&
K=3 , N=3 , F=256 , M=128
Depthwise Separable
3"×3×256"×128 = 226,492,416 3"×256"×3 + 3×256"×128 = 26,935,296
3"×128×3 = 3,456 3"×3 + 3×128 = 411
Depthwise Conv

8. Conv33
Conv33
Conv33
Maxpooling
22
Conv33
Maxpooling
22
Conv33
Conv33
Conv33
Maxpooling
22
Conv33
Conv33
Conv33
Flatten
FC(1024)
FC(512)
FC(2)
VGG16
DSConv 3 3
VGG16
ver.depthwise
VGG16 VGG16 ver.depthwise
8,689,474 1,927,645
(s / step) 0.349 0.121
(%) 74.2 79.4
Batch size =16

9. Depth:575
Input
(2992993)
Stem
5Convolution
Block-A
Reduction-A
10Convolution
Block-B
Reduction-B
5Convolution
Block-C
GlobalAverage
Pooling
Output
(Softmax)
2Convolution
Output:2
Depthwise Separable Convolution
Global Average Pooling Flatten
Fully Convolutional Net
Skip Connection
Inception Module
ImageNet
Deep Pneumonia Net

10. Conv 3 3
stride 2
Conv 3 3
Max pooling
3 3 stride 2
Conv 3 3
Conv 1 1
Conv 3 3
Conv 3 3
stride 2
Stem
ReLU
Conv 1 1 Conv 1 1
DSConv
3 3
Conv 1 1
DSConv
3 3
DSConv
3 3
Conv 1 1
ReLU
+
Convolution Block - A
Skip Connection
Inception Module

11. Previous
layer
Max pooling
3 3 stride 2
DSConv
3 3 stride 2
DSConv
3 3
Conv 1 1
DSConv
3 3 stride 2
Filter
concatenate
Reduction-A
ReLU
ReLU
Conv 1 1
DSConv
7 1
DSConv
1 7
Conv 1 1
+
Conv 1 1
Convolution Block - B
Skip Connection
Inception Module
Inception Module

12. Previous
layer
Conv 1 1
Max pooling
3 3 stride 2
DSConv
3 3 stride 2
DSConv
3 3 stride 2
Conv 1 1
Conv 1 1
DSConv
3 3 stride 2
DSConv
3 3
Filter
concatenate
Reduction-B Convolution Block - C
Inception Module
ReLU
ReLU
Conv 1 1
DSConv
3 1
DSConv
1 3
Conv 1 1
+
Conv 1 1
Inception Module
Skip
Connection

13. 5216
16 624
CNN CNN CNN
Inception-ResNet-v2
ImageNet

14. •
VGG16:16 InceptionResNetv2:572
Deep Pneumonia Net:575
• InceptionResNetv2
Depthwise
Separable Convolution
0
20
40
60
80
100
1
74.2
93.8 98.1
Accuracy(%)
Classifier performance on the test data
VGG16 InceptionResNetv2 Deep Pneumonia Net

15. Confusion matrix for Deep Pneumonia Net Confusion matrix for Inception Resnet v2
Accuracy:96.5% Accuracy:92.0%
Recall:97.3% Recall:94.4%
223 11
11 379
206
368
28
22
truelabel
truelabel
predicted labelpredicted label

16. •
•
1341
3875