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Research on Early Prediction of Lung Cancer Based on Deep Learning

Zhijun Qu

Zhijun Qu

School of Computer Science and Engineering Xi’an Technological University Xi’anChina
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Qu, Zhijun
 y 
Zhongsheng Wang

Zhongsheng Wang

State and Provincial Joint Engineering Lab. of Advanced Network, Monitoring and Control Xi’an Technological University Xi’anShaanxi, China
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Wang, Zhongsheng
  
16 jun 2025
Research on Early Prediction of Lung Cancer Based on Deep Learning's Cover Image
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Publicado en línea: 16 jun 2025
Páginas: 30 - 42
DOI: https://doi.org/10.2478/ijanmc-2025-0014
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© 2025 Zhijun Qu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The basic architecture of a convolutional neural network
The basic architecture of a convolutional neural network

Figure 2.

Convolution Process Diagram
Convolution Process Diagram

Figure 3.

Pooling Process Diagram
Pooling Process Diagram

Figure 4.

VGG16 Network Architecture Diagram
VGG16 Network Architecture Diagram

Figure 5.

Residual Connection Structure Diagram
Residual Connection Structure Diagram

Figure 6.

Transformer Architecture Diagram
Transformer Architecture Diagram

Figure 7.

Self-Attention Mechanism Computation Diagram
Self-Attention Mechanism Computation Diagram

Figure 8.

ViT Architecture Diagram
ViT Architecture Diagram

Figure 9.

DeiT Architecture Diagram.
DeiT Architecture Diagram.

Figure 10.

Randomly selected training sample images
Randomly selected training sample images

Figure 11.

Accuracy and Loss Curves of theVgg16
Accuracy and Loss Curves of theVgg16

Figure 12.

Accuracy and Loss Curves of the ResNet50
Accuracy and Loss Curves of the ResNet50

Figure 13.

Accuracy and Loss Curves of the DeiT
Accuracy and Loss Curves of the DeiT

Figure 14.

Confusion Matrix
Confusion Matrix

Figure 15.

Random Test Plot
Random Test Plot

EXPERIMENTAL DATASET TABLE

Training Set Validation Set Test Set
lung_aca 3500 750 750
lung_scc 3500 750 750
lung_n 3500 750 750

Prediction Metrics for Different Models

Model Acc(%) Average Precision(%) Average Recall(%) Average F1-Score(%)
Vgg16 98.49 98.49 98.49 98.49
Resnet50 97.51 97.51 97.51 97.51
DeiT 99.96 99.96 99.96 99.96
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