Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network

These results indicate improved diagnostic efficiency and support clinical decision making by providing more accurate and interpretable segmentation outputs. Moreover, VHUCS-Net is validated with additional publicly available datasets with image mask correspondence, therefore proving the model effectiveness and generalizability across many segmentation tasks. The results highlight the capability of the proposed VHUCS-Net model to enhance diagnostic accuracy and assist clinical decision making through more detailed and interpretable segmentation outcomes.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2026-02-04
DOI
https://doi.org/10.3389/frai.2026.1716063
Citations
1
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
46.84
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article

Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network

J. Jenifa Sharon, L. Jani Anbarasi
1 citations
Frontiers in Artificial Intelligence
Advanced Neural Network Applications
46.84
article

Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network

J. Jenifa Sharon, L. Jani Anbarasi
article en
1 citations

Abstract

These results indicate improved diagnostic efficiency and support clinical decision making by providing more accurate and interpretable segmentation outputs. Moreover, VHUCS-Net is validated with additional publicly available datasets with image mask correspondence, therefore proving the model effectiveness and generalizability across many segmentation tasks. The results highlight the capability of the proposed VHUCS-Net model to enhance diagnostic accuracy and assist clinical decision making through more detailed and interpretable segmentation outcomes.

Frontiers in Artificial IntelligenceVol. 9
Vellore Institute of Technology University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 1%
Advanced Neural Network Applications
46.84
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