An Improved Artificial Intelligence Enabled Diffusion Models for Accurate Lung Nodule Segmentation in Early Screening Stage

Lung nodules are significant in the screening of lung cancer since early identification of lung nodules is essential in facilitating timely diagnosis and management. Nevertheless, the correct segmentation of small or indistinct nodules in low-dose CT is still a chronic problem because of noise, imbalance in classes, and variations in shapes. The proposed study aims to develop a better Artificial Intelligence-based Diffusion Model to be used in the precise segmentation of lung nodules at the early stages of screening. We combine a conditional denoising diffusion probabilistic model and a U-Net-based backbone with time-step embeddings, cross-view attention, and a hybrid loss based on Dice, Focal, and KL divergence losses. The model is trained and tested on the LUNA16 dataset, with a Dice score of 92.56, an IoU of 86.34, Hausdorff Distance of 1.98mm, and Sensitivity of 91.72, which are better than the most recent state-of-the-art baselines, such as TransUNet or Swin U-Net. The role of every module is verified in an ablation study, and the cosine annealed noise scheduler improves the accuracy of denoising. Moreover, our model helps the efficient inference (\~{}13.9 FPS) that is applicable to the edge deployment. These findings indicate that the suggested model is robust in detecting small or rare nodules and has some potential in low-resource clinical environments. The further work involves multi-class lung disease segmentation model extension and validation of its use in real-life hospitals.