  Medical imaging plays a crucial role in modern healthcare by providing
non-invasive visualisation of internal structures and abnormalities, enabling
early disease detection, accurate diagnosis, and treatment planning. This study
aims to explore the application of deep learning models, particularly focusing
on the UNet architecture and its variants, in medical image segmentation. We
seek to evaluate the performance of these models across various challenging
medical image segmentation tasks, addressing issues such as image
normalization, resizing, architecture choices, loss function design, and
hyperparameter tuning. The findings reveal that the standard UNet, when
extended with a deep network layer, is a proficient medical image segmentation
model, while the Res-UNet and Attention Res-UNet architectures demonstrate
smoother convergence and superior performance, particularly when handling fine
image details. The study also addresses the challenge of high class imbalance
through careful preprocessing and loss function definitions. We anticipate that
the results of this study will provide useful insights for researchers seeking
to apply these models to new medical imaging problems and offer guidance and
best practices for their implementation.
