T-M Net: A Fusion Convolutional Neural Network Method for Lung Aging Assessment

As the population ages, the precise assessment of pulmonary senescence becomes essential for the diagnosis and management of chronic respiratory diseases. However, existing medical imaging analysis techniques encounter significant chal-lenges due to data scarcity, especially in the domain of lung CT imaging. This research proposes an innovative method for assessing lung aging in CT images-T-M Net. This method combines transfer learning and MS-CNN to improve the accuracy and efficiency of lung aging assessment in CT images. We employed a pretrained ResNet18 model, capitalizing on transfer learning to harness the insights from extensive datasets, thereby minimizing the computational demands of model training. Furthermore, a channel attention mechanism is integrated within the transfer learning framework to heighten the model's focus on critical pulmonary regions, thus augmenting its predictive accuracy and interpretability. To accommodate the distinctive characteristics of lung CTs, a MS CNN architecture was developed, capturing multi-dimensional information ranging from microscopic details to macroscopic structures through variously sized convolutional kernels. Testing on a lung CT dataset revealed that our approach surpasses traditional methods in accuracy and generalizability, underscoring its potential utility in the early diagnosis and effective treatment of pulmonary ailments.