BiT-Based Early Gastric Cancer Classification Using Endoscopic Images

Fig. 1. Comparison of four metrics among the models with different backbone networks, the spot size represents the number of trainable parameters of each model. (a) Accuracy; (b) F1-score; (c) $s e n s i t i v i t y$ ; (d) $s p e c i f i c i t y$

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Fig. 2. ROC curves and AUC of different models. (a) ROC curves of five models with different untrainable backbone networks; (b) ROC curves of four models with different trainable backbone networks; (c) ROC curves of six models with a trainable 50×1 backbone network under different batchsizes

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Fig. 3. Confusion matrices of all the models applied on the test set, C represents the cancer label, NC represents the non-cancer label

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Fig. 4. Examples of testing images

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Table 1. Batchsizes and parameter amounts of the five BiT backbone networks
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Table 1. Batchsizes and parameter amounts of the five BiT backbone networks
Parameter 50×1 101×1 50×3 101×3 152×4
Batchsize 32 16 8 4 4*
Number of parameters 23504450 42496578 211186370 381802178 928356610

Table 2. Testing metrics of the models with different backbone networks on test set

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Table 2. Testing metrics of the models with different backbone networks on test set

Size of backbone network	Trainable state	Accuracy /%	Sensitivity /%	Specificity /%	F1-score /%
50×1	TRUE	95.04	89.33	97.33	91.16
50×1	FALSE	92.37	84.67	95.45	86.39
101×1	TRUE	94.47	86.67	97.59	89.97
101×1	FALSE	94.08	88.67	96.26	89.56
50×3	TRUE	95.99	93.33	97.06	93.02
50×3	FALSE	93.13	83.33	97.06	87.41
101×3	TRUE	97.14	90.67	99.73	94.77
101×3	FALSE	94.85	88.00	97.59	90.72
152×4	FALSE	94.08	82.67	98.66	88.89

Table 3. Four metrics of the six models with a trainable 50×1 backbone network under different batchsizes
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Table 3. Four metrics of the six models with a trainable 50×1 backbone network under different batchsizes
Batchsize Accuracy /% Sensitivity /% Specificity /% F1-score /%
32 95.04 89.33 97.33 91.16
16 95.23 89.33 97.59 91.47
8 95.23 90.00 97.33 91.53
4 93.89 89.33 95.72 89.33
2 95.23 88.67 97.86 91.41
1 95.42 90.00 97.59 91.84

Table 4. The Pearson correlation scores and their p-values between the four metrics and the batchsize in Table 3
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Table 4. The Pearson correlation scores and their p-values between the four metrics and the batchsize in Table 3
Parameter Accuracy Sensitivity Specificity F1-score
Correlation score 0.0835 -0.0784 0.1056 0.0753
p-value 0.8750 0.8826 0.8423 0.8873

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Hongxiao Li, Shu Li, Xiafei Shi, Xiaoxi Dong, Ge Jin, Lanping Zhu, Yingxin Li, Huijuan Yin. BiT-Based Early Gastric Cancer Classification Using Endoscopic Images[J]. Laser & Optoelectronics Progress, 2022, 59(6): 0617028

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Paper Information

Category: Medical Optics and Biotechnology

Received: Nov. 15, 2021

Accepted: Jan. 7, 2022

Published Online: Mar. 8, 2022

The Author Email: Huijuan Yin (yinzi490@163.com)

DOI:10.3788/LOP202259.0617028

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Batchsizes and parameter amounts of the five BiT backbone networks

Table 1. Batchsizes and parameter amounts of the five BiT backbone networks

Table 2. Testing metrics of the models with different backbone networks on test set

Table 2. Testing metrics of the models with different backbone networks on test set

Table 3. Four metrics of the six models with a trainable 50×1 backbone network under different batchsizes

Table 3. Four metrics of the six models with a trainable 50×1 backbone network under different batchsizes

Table 4. The Pearson correlation scores and their p-values between the four metrics and the batchsize in Table 3

Table 4. The Pearson correlation scores and their p-values between the four metrics and the batchsize in Table 3