Trans RCED-UNet3+: a hybrid CNN-transformer model for precise lung nodule segmentation

Introduction: Precisely segmenting lung nodules in CT scans is essential for diagnosing lung cancer, though it is challenging due to the small size and intricate shapes of these nodules. Methods: This study presents Trans RCED-UNet3+, an enhanced version of the RCED-UNet3+ framework designed to address these challenges. The model features a transformer-based bottleneck that captures global context and long-range dependencies, along with residual connections that facilitate efficient feature flow and prevent gradient loss. To improve boundary accuracy, we employ a hybrid loss function that combines Dice loss with Binary Cross-Entropy, enhancing the clarity of nodule edges. Results: Evaluation on the LIDC-IDRI dataset demonstrates a notable advancement, as Trans RCED-UNet3+ achieves a Dice score of 0.990, exceeding the original model's score of 0.984. Discussion: These findings underscore the value of merging convolutional and transformer architectures, delivering a robust approach for precise segmentation in medical imaging. This model enhances the detection of subtle and irregular structures, enabling more accurate lung cancer diagnoses in clinical environments.