Trans RCED-UNet3+: a hybrid CNN-transformer model for precise lung nodule segmentation - Raza, Sadaf; Zia, Razia; Usmani, Irfan Ahmed; Almujally, Nouf Abdullah; Alasbali, Nada; Hanif, Muhammad (Frontiers in Oncology, 2025)
IntroductionPrecisely 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. MethodsThis 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.ResultsEvaluation 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.DiscussionThese 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.