Objective To achieve efficient and accurate detection of non-small cell lung cancer(NSCLC)based on chest CT images,a deep learning algorithm based on the EfficientNetB0-CBAM model is proposed.Methods A dataset of lung cancer image classification was constructed using 1000 CT images of four types.EfficientNetB0 was chosen as the base classifi-cation model,and the CBAM attention mechanism was integrated into EfficientNetB0 to build the EfficientNetB0-CBAM mod-el,which enhances important features and suppresses irrelevant ones.Batch normalization and random dropout modules were used to accelerate network training and alleviate overfitting.Accuracy,precision,recall,and F1-Score were used as perform-ance evaluation metrics to test the model's performance on the test set,and the results were compared with other state-of-the-art(SOTA)models(VGG16,ResNet50,MobileNet,DenseNet121,ConvNeXtTiny).Results The EfficientNetB0-CBAM model achieved 95.56%accuracy,95.49%precision,96.13%recall,and 95.74%F1-Score,outperforming other deep learning methods used in the comparison experiments.The Grad-CAM visualization method showed that the EfficientNetB0-CBAM mod-el focuses more on lesion areas that play a key role in recognition.Conclusion This study provides technical support for the automatic detection of NSCLC using deep learning and offers a basis for the early diagnosis of lung cancer.
关键词
肺癌检测/深度学习/注意力机制/非小细胞肺癌
Key words
Lung cancer detection/Deep learning/Attention mechanism/Non-small cell lung cancer
维普
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