Convolutional Neural Network Based on DenseNet Evolution for Image Classification Algorithm

Term	Parameter setting
Learning rate	[0.001,0.1]
Dropout rate	[0,0.1]
Number of filters in 2D convolution	[4,6,18]
Filter size for 2D convolution	[2,3]
Number of units	[64,128,256]
Layer or block	[2D convolution, fully connected layers, DenseNet]
Activation function of 2D convolutional layer	[Leaky ReLU, RelU, PReLU, ReLU]
Activation function of the fully connected layer	[Sigmoid, Softmax, ReLU]
Activation function of the last fully connected layer	Sigmoid

Table 2. Structure of D-ECNN model

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Table 2. Structure of D-ECNN model

Layer	D-ECNN	Output channel
2D convolutional layer	Conv(3×3)	64
2D convolutional layer	Conv(2×2)	16
Dense block	Conv(1×1)Conv(3×3)	8
	Conv(1×1)Conv(3×3)	8
	Conv(1×1)Conv(3×3)	8
Transition layer	Conv(1×1)average pool(2×2)	4
Fully connected layer	Sigmoid	64
Fully connected layer	Sigmoid	2

Table 3. Test performances of D-ECNN model under different segmentation rates unit: %
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Table 3. Test performances of D-ECNN model under different segmentation rates unit: %
Rate Accuracy Recall Precision F1-score
9∶1 95.78 95.50 71.73 81.92
8∶2 95.15 94.50 84.94 89.48
7∶3 95.36 94.31 90.62 92.43
6∶4 95.39 94.75 93.78 94.26
Average 95.42 94.77 85.28 89.52

Table 4. Performance indicators of D-ECNN and VGG16 algorithms unit: %
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Table 4. Performance indicators of D-ECNN and VGG16 algorithms unit: %
Algorithm Accuracy Recall Precision F1-score
D-ECNN 95.34 94.88 95.77 95.32
VGG16 94.56 92.63 96.36 94.46

Table 5. Train parameters of the two algorithms in the same train set

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Table 5. Train parameters of the two algorithms in the same train set

Algorithm	Number of network layers	Number of network parameters	Number of training parameters	Model file size /M	Time-consuming of the test data set /s
D-ECNN	11	70939	69703	1.04	0.0345
VGG16	16	27844930	27834434	212	0.3967

Table 6. Parameters of 10 algorithm models

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Table 6. Parameters of 10 algorithm models

Algorithm model	Number of layers	Learning rate	Accuracy /%
D-ECNN	11	0.0050	95.34
Model-1	18	0.0035	94.53
Model-2	19	0.0037	93.42
Model-3	13	0.0059	94.22
Model-4	19	0.0047	90.73
Model-5	11	0.0045	68.76
Model-6	19	0.0055	94.82
Model-7	5	0.0058	94.38
Model-8	18	0.0055	94.83
Model-9	6	0.0045	95.23

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Yongjie Ma, Peipei Liu. Convolutional Neural Network Based on DenseNet Evolution for Image Classification Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241001

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