3D-CA Net: a 3D-CNN with composite multidimensional attention for the assessment of lung aging

As the population ages, early diagnosis and treatment of lung diseases become increasingly important. Accurate assessment of aging-related changes in lung CT images is crucial for the prevention and treatment of related diseases. Traditional methods for lung aging assessment from CT images are time-consuming, subjective, and heavily reliant on the clinical experience of doctors. To address these issues, this paper proposes a lung aging assessment method with 3D-CA Net. The feature extraction part of the proposed network consists of four main 3D Convolutional and Composite Multidimensional Attention Modules. By introducing the Composite Multidimensional Attention Module, the advantages of spatial attention and self-attention are both utilized. Additionally, an improved E-cross-entropy loss function is employed to reduce overfitting and enhance generalization. Experimental results demonstrate that the 3D-CA Net significantly outperforms existing methods in terms of accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score. This work provides a comprehensive solution for lung CT image aging assessment and offers insights for future advancements in medical imaging analysis.