Comparative Analysis of Vision Transformers and U-Net for Medical Image Segmentation in Early Disease Detection: A Deep Learning Approach

Medical image segmentation remains a critical challenge in computer-aided diagnosis systems, particularly for early disease detection where precise boundary delineation can significantly impact patient outcomes. This study presents a comprehensive comparative analysis between Vision Transformer (ViT) based architectures and the conventional U-Net model for multi-organ segmentation tasks using chest CT scans and retinal fundus images. We evaluated both architectures on three distinct datasets comprising 15,420 annotated medical images, focusing on lung nodule detection, liver lesion segmentation, and retinal vessel segmentation for diabetic retinopathy screening. Our experimental results demonstrate that while U-Net achieves superior performance on smaller datasets (Dice coefficient: 0.89 ± 0.03), Vision Transformers exhibit remarkable capabilities with larger training samples (Dice coefficient: 0.93 ± 0.02), showing 4.5% improvement in segmentation accuracy. The ViT-based approach demonstrated enhanced generalization capabilities across diverse imaging modalities, reducing false positive rates by 31% compared to U-Net in cross-dataset validation. Furthermore, computational efficiency analysis revealed that despite requiring 2.3× more training time, ViT models reduced inference time by 18% in clinical deployment scenarios. Performance evaluation across image quality levels showed ViT maintained more consistent performance across signal-to-noise ratios (Dice drop: 4.2% from high to low SNR) compared to U-Net (8.7% drop), demonstrating transformers' robustness to image degradation in clinical settings where scan quality varies. These findings suggest that the choice between architectures should be guided by dataset size, computational resources, and specific clinical requirements, with hybrid approaches showing promising potential for future development.