mlsanityData to 14 Oct 2026

Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis

arXiv 2602.15067 ↗First posted 14 Feb 2026cs.AIcited by 0

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Abstract (arXiv)

Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based Triplanar (2.5D) model for improved brain tumor segmentation. The proposed model enhances feature representation and segmentation accuracy by integrating residual, recurrent, and triplanar architectures while maintaining computational efficiency, potentially aiding in better treatment planning. The proposed method achieves a Dice Similarity Score (DSC) of 0.900 for Whole Tumor (WT) segmentation on the BraTS2021 validation set, demonstrating performance comparable to leading models. Additionally, the triplanar network extracts 64 features per planar model for survival days prediction, which are reduced to 28 using an Artificial Neural Network (ANN). This approach achieves an accuracy of 45.71%, a Mean Squared Error (MSE) of 108,318.128, and a Spearman Rank Correlation Coefficient (SRC) of 0.338 on the test dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 51 references that are in the map. First posted on arXiv 14 Feb 2026.

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References · 25

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13 more
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