Automatic Phase Recognition Method Based on Convolutional Neural Network

Fig. 1. Examples of each class in training dataset (left) and testing dataset (right) (a) Red blood cell; (b) polystyrene bead; (c) small lymphocyte; (d) noise map

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Fig. 2. Structure of CNN implemented for 4-class phase recognition

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Fig. 3. Performance of LeNet-5 model in training process. (a) Loss function value; (b) accuracy

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Fig. 4. Phase distributions of red blood cell before and after change. (a) Before change; (b) after change

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Fig. 5. Performance of new CNN model in training process. (a) Loss function value; (b) accuracy

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Fig. 6. Interferograms of various samples. (a) Red blood cell; (b) polystyrene bead; (c) small lymphocyte; (d) noise map

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Fig. 7. Performance of LeNet-5 model in training process. (a) Loss function value; (b) accuracy

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Fig. 8. ResNet-17 network architecture. (a) Structure of residual block; (b) total architecture of network

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Fig. 9. Performance of LeNet-5 model in training process. (a) Loss function value; (b) accuracy

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Fig. 10. Polystyrene bead interferograms with different intensity ratios (reference wave∶object wave). (a) 1∶1; (b) 2∶1; (c) 3∶1; (d) 4∶1

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Fig. 11. Polystyrene bead interferograms with different fringe spatial frequencies. (a) 1.05 rad/pixel; (b) 1.57 rad/pixel; (c) 2.09 rad/pixel; (d) 3 rad/pixel

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Fig. 12. Performance of LeNet-5 model in training process. (a) Loss function value; (b) accuracy

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Table 1. Confusion matrix of classification results of LeNet-5 model on interferogram testing dataset
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Table 1. Confusion matrix of classification results of LeNet-5 model on interferogram testing dataset
Confusion matrix Predicted class
Red blood cell Polystyrene bead Small lymphocyte No phase object
Actual class Red blood cell 28 22
Polystyrene bead 48 2
Small lymphocyte 15 35
No phase object 6 44

Table 2. Performance of LeNet-5 model on each class of interferogram testing dataset
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Table 2. Performance of LeNet-5 model on each class of interferogram testing dataset
Class Accuracy /% Recall /% F1-Score Overall accuracy /%
Red blood cell 82.35 56 0.667 77.5
Polystyrene bead 76.19 96 0.850
Small lymphocyte 94.59 70 0.805
No phase object 66.67 88 0.759

Table 3. Performance of ResNet-17 model on each class of interferogram testing dataset
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Table 3. Performance of ResNet-17 model on each class of interferogram testing dataset
Class Accuracy /% Recall /% F1-Score Overall accuracy /%
Red blood cell 100 100 1.000 98
Polystyrene bead 100 92 0.958
Small lymphocyte 92.59 100 0.962
No phase object 100 100 1.000

Table 4. Performance of ResNet-17 model on interferogram datasets with different fringe spatial frequencies

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Table 4. Performance of ResNet-17 model on interferogram datasets with different fringe spatial frequencies

Datasets with different intensity ratios（R∶O）	Different fringe spatial frequency datasets /（rad⋅pixel^-1）	Correctly identify number of samples/number of all samples		Accuracy /%
1∶1	1.05	863/867	453/867	99.54	52.25
2∶1	1.57	849/867	863/867	97.92	99.54
3∶1	2.09	849/867	361/867	97.92	41.64
4∶1	3	840/867	654/867	96.89	75.43

Table 5. Performance of ResNet-17 model on each class of interferogram testing dataset with multiple spatial frequencies
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Table 5. Performance of ResNet-17 model on each class of interferogram testing dataset with multiple spatial frequencies
Class Accuracy /% Recall /% F1-Score Overall accuracy /%
Red blood cell 100 100 1.000 97.25
Polystyrene bead 90.09 100 0.948
Small lymphocyte 100 89 0.942
No phase object 100 100 1.000

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Ying Ji, Lingran Gong, Shuang Fu, Yawei Wang. Automatic Phase Recognition Method Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(6): 0617026

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Paper Information

Category: Medical Optics and Biotechnology

Received: Jun. 28, 2021

Accepted: Aug. 31, 2021

Published Online: Mar. 8, 2022

The Author Email: Ying Ji (jy@ujs.edu.cn)

DOI:10.3788/LOP202259.0617026

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Confusion matrix of classification results of LeNet-5 model on interferogram testing dataset

Table 1. Confusion matrix of classification results of LeNet-5 model on interferogram testing dataset

Table 2. Performance of LeNet-5 model on each class of interferogram testing dataset

Table 2. Performance of LeNet-5 model on each class of interferogram testing dataset

Table 3. Performance of ResNet-17 model on each class of interferogram testing dataset

Table 3. Performance of ResNet-17 model on each class of interferogram testing dataset

Table 4. Performance of ResNet-17 model on interferogram datasets with different fringe spatial frequencies

Table 4. Performance of ResNet-17 model on interferogram datasets with different fringe spatial frequencies

Table 5. Performance of ResNet-17 model on each class of interferogram testing dataset with multiple spatial frequencies

Table 5. Performance of ResNet-17 model on each class of interferogram testing dataset with multiple spatial frequencies