  This study provides a comparative analysis of deep learning models including
UNet, Res-UNet, Attention Res-UNet, and nnUNet, and evaluates their performance
in brain tumour, polyp, and multi-class heart segmentation tasks. The analysis
focuses on precision, accuracy, recall, Dice Similarity Coefficient (DSC), and
Intersection over Union (IoU) to assess their clinical applicability. In brain
tumour segmentation, Res-UNet and nnUNet significantly outperformed UNet, with
Res-UNet leading in DSC and IoU scores, indicating superior accuracy in tumour
delineation. Meanwhile, nnUNet excelled in recall and accuracy, which are
crucial for reliable tumour detection in clinical diagnosis and planning. In
polyp detection, nnUNet was the most effective, achieving the highest metrics
across all categories and proving itself as a reliable diagnostic tool in
endoscopy. In the complex task of heart segmentation, Res-UNet and Attention
Res-UNet were outstanding in delineating the left ventricle, with Res-UNet also
leading in right ventricle segmentation. nnUNet was unmatched in myocardium
segmentation, achieving top scores in precision, recall, DSC, and IoU. The
conclusion notes that although Res-UNet occasionally outperforms nnUNet in
specific metrics, the differences are quite small. Moreover, nnUNet
consistently shows superior overall performance across the experiments.
Particularly noted for its high recall and accuracy, which are crucial in
clinical settings to minimize misdiagnosis and ensure timely treatment,
nnUNet's robust performance in crucial metrics across all tested categories
establishes it as the most effective model for these varied and complex
segmentation tasks.
