EF-VPT-Net: Enhanced Feature-Based Vision Patch Transformer Network for Accurate Brain Tumor Segmentation in Magnetic Resonance Imaging

Brain tumor segmentation and the accurate detection of tumor types remain significant challenges due to image variability, tumor heterogeneity, and boundary demarcation difficulties. These challenges are exacerbated by limited annotated datasets, class imbalances, and the high computational cost of existing methods. Our study introduces the enhanced feature (EF)-based vision patch transformer network algorithm, which uses an EF module-based customized U-Net architecture. This architecture consists of an optimized vision patch transformer network as the encoder, while the decoder phase utilizes positional embedding-based convolutional U-Net blocks to identify the segmented edges of different types of tumor images accurately. We incorporated additional layers designed for efficient processing of different block sizes with positional embedding to ensure adaptability to different image resolutions and enhance the model's generality. By using the transformer as the encoder of the U-Net, we optimized the model's ability to capture global context and long-range dependencies early in the processing pipeline, thereby improving segmentation performance. In our study, we use two public datasets. Performance indicators, such as accuracy and sensitivity, show accurate segmentation up to 99.1%. Our research contributes to the continuous progress of medical image segmentation and provides promising insights for improving diagnosis and treatment planning in healthcare.

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