Computed Tomography Image Classification Algorithm Based on Improved Deep Residual Network

Table 1. Different dataset separation schemes
View table
Table 1. Different dataset separation schemes
Scheme Size of training set Size of test set Split manner
A 4132 1031 5-fold
B 2560 2479 Case select
C 11289 750 Random select

Table 2. Comparison of classification results of SK-ResNet with different depths
View table
Table 2. Comparison of classification results of SK-ResNet with different depths
Network Structure ofResNet block f_avg
A B C
SK-ResNet 10 [1,1,1,1] 0.9586 0.9453 0.9651
SK-ResNet 14 [2,2,1,1] 0.9609 0.9476 0.9640
SK-ResNet 18 [2,2,2,2] 0.9533 0.9354 0.9553
SK-ResNet 34 [3,4,6,3] 0.9497 0.9329 0.9549

Table 3. Comparison of results on SK-ResNet before and after using transfer learning
View table
Table 3. Comparison of results on SK-ResNet before and after using transfer learning
Method A B C
Without transfer 0.9609 0.9476 0.9640
DIM 0.9799 0.9654 0.9707
DIM+PM 0.9818 0.9677 0.9756

Table 4. Classification confusion matrix of SK-ResNet
View table
Table 4. Classification confusion matrix of SK-ResNet
Ground truth Predicted result
EM FB GG NM MN
EM 0.91 0 0 0.08 0.01
FB 0 0.96 0.04 0 0
GG 0 0.02 0.98 0 0
NM 0 0 0.03 0.94 0.03
MN 0 0 0 0.06 0.94

Table 5. Classification confusion matrix of SK-ResNet using transfer learning
View table
Table 5. Classification confusion matrix of SK-ResNet using transfer learning
Ground truth Predicted result
EM FB GG NM MN
EM 0.97 0 0 0.03 0
FB 0 0.96 0.04 0 0
GG 0 0.02 0.98 0 0
NM 0 0 0.03 0.96 0.01
MN 0 0 0 0.03 0.97

Table 6. Comparison of classification performances of SK-ResNet and other methods

View table

Table 6. Comparison of classification performances of SK-ResNet and other methods

Method	A	B	C
Ref.[14]	0.7725	0.7644	0.7987
Ref. [16]	0.9465	0.9215	0.9392
Ref. [23]	0.9483	0.9269	0.9554
AlexNet^[11]	0.8962	0.8821	0.9226
Pre-trained AlexNet^[11]	0.9471	0.9337	0.9609
Pre-trained VGG-16^[24]	0.9603	0.9435	0.9664
ResNet-18^[17]	0.9371	0.9306	0.9432
Pre-trained ResNet-18^[17]	0.9581	0.9443	0.9651
Pre-trained DenseNet-121^[25]	0.9741	0.9579	0.9715
SK-ResNet	0.9609	0.9476	0.9640
SK-ResNet (transfer)	0.9818	0.9677	0.9756

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Sheng Huang, Feifei Li, Qiu Chen. Computed Tomography Image Classification Algorithm Based on Improved Deep Residual Network[J]. Acta Optica Sinica, 2020, 40(3): 0310002

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Paper Information

Category: Image Processing

Received: Sep. 3, 2019

Accepted: Oct. 21, 2019

Published Online: Feb. 10, 2020

The Author Email: Feifei Li (feifeilee@ieee.org), Qiu Chen (q.chen@ieee.org)

DOI:10.3788/AOS202040.0310002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Different dataset separation schemes

Table 1. Different dataset separation schemes

Table 2. Comparison of classification results of SK-ResNet with different depths

Table 2. Comparison of classification results of SK-ResNet with different depths

Table 3. Comparison of results on SK-ResNet before and after using transfer learning

Table 3. Comparison of results on SK-ResNet before and after using transfer learning

Table 4. Classification confusion matrix of SK-ResNet

Table 4. Classification confusion matrix of SK-ResNet

Table 5. Classification confusion matrix of SK-ResNet using transfer learning

Table 5. Classification confusion matrix of SK-ResNet using transfer learning

Table 6. Comparison of classification performances of SK-ResNet and other methods

Table 6. Comparison of classification performances of SK-ResNet and other methods