EnsUNet : Enhancing Brain Tumor Segmentation Through Fusion of Pre-trained Models

• Brain tumor segmentation, among various tasks in medical image analysis, has garnered significant attention in the research community. Despite continuous efforts by researchers, accurate brain tumor segmentation remains a key challenge. This challenge arises due to various factors, including location uncertainty, morphological uncertainty, low contrast imaging, annotation bias, and data imbalance. Magnetic resonance imaging (MRI) plays a vital role in providing detailed images of the brain, enabling the extraction of crucial information about the tumor’s shape, size, and location. In literature, deep learning algorithms have shown their efficiency in dealing with semantic segmentation, particularly the U-Net architecture. The latter has demonstrated impressive performance in Medical image segmentation. In this paper, a U-Net-based architecture for brain tumor segmentation is proposed. To further enhance the segmentation performance of our model, a novel ensemble learning method, EnsUNet, is introduced by integrating four pre-trained networks namely MobileNet, DeepLabV3+, ResNet, and DenseNet as the encoder within the U-Net architecture. The conducted experimental evaluation demonstrates promising results, achieving an Intersection over Union (IoU) score of 0.86, a Dice Coefficient (DC) of 0.92, and an accuracy of approximately 0.99. These findings underscore the effectiveness of our proposed EnsUNet for accurately segmenting brain tumors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

Nyckelord

Brain tumor segmentation

Ensemble learning

EnsUNet

Magnetic resonance imaging

Pre-trained models

U-Net

Brain

Deep learning

Learning algorithms

Learning systems

Medical imaging

Network architecture

Semantic Segmentation

Semantics

Tumors

Location uncertainty

Medical image analysis

NET architecture

Pre-trained model

Research communities

Uncertainty

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