A Comparative Analysis of Medical Image Segmentation utilizing U-Net Architectures: EfficientNet-B7, Half U-Net, and Nested U-Net

UNet has become an essential medical image segmentation and disease detection framework, offering robust solutions to intricate imaging challenges. The UNet architecture is specifically designed to efficiently handle medical images, even with limited training data, making it a cornerstone in various imaging techniques like CT, MRI, and X-ray. Complementing UNet, variants such as Half-Unet and Nested-Unet have emerged, each introducing enhancements for improved performance. Half-Unet simplifies the network architecture, lowering computational demands while preserving segmentation accuracy. This makes it well-suited for real-time applications and environments with limited resources. Nested-Unet refines segmentation through a series of densely connected layers and improved feature maps, addressing the issues of overfitting and better handling of small structures within medical images. This review offers a comprehensive comparison of the merits and limitations of UNet, Half-Unet, and Nested-Unet in the realm of medical image analysis. We delve into how advancements in deep learning, including feature optimization and network refinement, have enhanced the capabilities of these architectures. Additionally, we explore real-world case studies, showcasing their applications across various medical fields. Our analysis provides insights into how the integration of unique features from these UNet variants can pave the way for more effective models in medical image segmentation.