Attention-Enhanced Nested U-Net with Fuzzy Pooling for Medical Image Analysis: A Review
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Keywords:
Medical Image Segmentation MRI, Attention mechanism, Conventional Neural networks, Deep learning models, Nested U-Net, Fuzzy Pooling, Intelligent Segmentation, Dice ScoreAbstract
Magnetic resonance imaging (MRI) is the cornerstone of medical diagnosis; however, accurate
segmentation of MRI images remains a challenging task due to noise, low contrast, and complex anatomical
structures. Traditional machine learning and early deep learning methods have achieved moderate success,
but they often struggle to maintain precise boundaries and handle ambiguous areas. Recent innovations,
such as attention mechanisms and multi-scale architectures like the Nested U-Net, have significantly
improved the accuracy of locating and segmenting features. Despite all this, traditional clustering processes
can still cause information loss, especially at object boundaries. In this review, the evolution of MRI
segmentation will be systematically explored through four developmental stages: (1) classical machine
learning, (2) convolutional neural networks (CNNs), (3) deep learning architectures, and (4) optimized
networks. Using attention and fuzzy logic. We will highlight the strengths and limitations of each stage,
and propose an advanced segmentation framework that combines an attention-enhanced nested U-Net with
fuzzy pooling, a technique that integrates soft decision-making to retain uncertain and boundary
information. Preliminary results show improved dice similarity coefficient (DSC) and sensitivity, as well
as decreased Hausdorff distance (HD), especially in complex MRI data sets of the brain and liver. Our
approach shows superior generalization and accuracy to traditional clustering strategies. Future work will
also focus on cross-media adaptation, real-time deployment in clinical settings, and integration of
automated diagnostics.
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