This article presents a deep learning-based approach for lung medical image recognition, focusing on the detection and classification of pulmonary nodules, which are crucial for early lung cancer diagnosis. The study introduces an enhanced neural network model that integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer to improve feature extraction. The model was evaluated on the LUNA16 dataset, achieving an accuracy of 88.78% and a sensitivity of 87.18%, which are higher than the traditional GoogLeNet Inception V3 algorithm by 2.7 and 2.22 percentage points, respectively. The experiments also demonstrated the model's superior generalization capabilities across different dataset proportions. The research highlights the importance of addressing the disparity between source and target datasets in transfer learning and proposes a solution through the enhanced network design. The findings suggest that this model can provide objective benchmarks for clinical diagnosis and improve the accuracy of lung cancer detection. The study also discusses the methodology, including dataset preprocessing, network weight updates using Stochastic Gradient Descent, and performance evaluation metrics such as accuracy, sensitivity, and specificity. The results indicate that the enhanced model outperforms existing methods, offering potential for clinical applications. The article concludes by emphasizing the need for further research to address overfitting and improve generalization, suggesting future work on integrating computational features with semantic attributes for more comprehensive diagnostic assessments.This article presents a deep learning-based approach for lung medical image recognition, focusing on the detection and classification of pulmonary nodules, which are crucial for early lung cancer diagnosis. The study introduces an enhanced neural network model that integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer to improve feature extraction. The model was evaluated on the LUNA16 dataset, achieving an accuracy of 88.78% and a sensitivity of 87.18%, which are higher than the traditional GoogLeNet Inception V3 algorithm by 2.7 and 2.22 percentage points, respectively. The experiments also demonstrated the model's superior generalization capabilities across different dataset proportions. The research highlights the importance of addressing the disparity between source and target datasets in transfer learning and proposes a solution through the enhanced network design. The findings suggest that this model can provide objective benchmarks for clinical diagnosis and improve the accuracy of lung cancer detection. The study also discusses the methodology, including dataset preprocessing, network weight updates using Stochastic Gradient Descent, and performance evaluation metrics such as accuracy, sensitivity, and specificity. The results indicate that the enhanced model outperforms existing methods, offering potential for clinical applications. The article concludes by emphasizing the need for further research to address overfitting and improve generalization, suggesting future work on integrating computational features with semantic attributes for more comprehensive diagnostic assessments.