A self-attention generative adversarial network(SAGAN)is proposed to improve the accuracy of histological subtype prediction for lung cancer cases.After collecting and preprocessing the lung cancer image dataset and data augmentation,SAGAN model is trained,where the generator uses self-attention mechanism to strengthen feature extraction,while the discriminator optimizes the generation process.Experimental results show that SAGAN model achieves accuracies of 0.852 and 0.845 on the training and test sets,respectively,with recall rates of 0.833 and 0.829,outperforming the other models.Additionally,the narrow confidence intervals indicate the high stability of SAGAN model in classification.SAGAN is helpful for lung cancer image analysis,providing stronger support for clinical decision-making.

A self-attention generative adversarial network(SAGAN)is proposed to improve the accuracy of histological subtype prediction for lung cancer cases.After collecting and preprocessing the lung cancer image dataset and data augmentation,SAGAN model is trained,where the generator uses self-attention mechanism to strengthen feature extraction,while the discriminator optimizes the generation process.Experimental results show that SAGAN model achieves accuracies of 0.852 and 0.845 on the training and test sets,respectively,with recall rates of 0.833 and 0.829,outperforming the other models.Additionally,the narrow confidence intervals indicate the high stability of SAGAN model in classification.SAGAN is helpful for lung cancer image analysis,providing stronger support for clinical decision-making.