Hybrid CNN-Transformer Model with Attention-Based Decoding for Accurate Abdominal Organ Segmentation in Medical Imaging

Medical image segmentation is important in diagnosing diseases and planning treatments. Abdominal organ segmentation is difficult due to variations in shape, size, and position. Traditional methods struggle with unclear boundaries and overlapping structures. Deep learning models improve segmentation but often miss details or fail with complex shapes. A new deep learning model is designed to improve accuracy. It combines convolutional networks with transformers to capture both local and global features. The model extracts multi-scale features to preserve fine details. A transformer module processes global relationships between organs. An attention-based decoder refines segmentation masks to enhance boundary detection. The model is tested on medical datasets containing CT and MRI scans. It is compared with leading segmentation methods using standard metrics. The results show improved accuracy and better-defined organ boundaries. The Dice similarity score increases by 3% for liver segmentation and 5% for kidney segmentation. The model also reduces false positives and improves consistency in segmenting complex structures. This method addresses key challenges in abdominal organ segmentation. It captures fine details while understanding organ relationships. The improved accuracy makes it useful for clinical applications.