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Improved U-Net architecture with VGG-16 for brain tumor segmentation

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Abstract

Automated assessment and segmentation of Brain MRI images facilitate towards detection of neurological diseases and disorders. In this paper, we propose an improved U-Net with VGG-16 to segment Brain MRI images and identify region-of-interest (tumor cells). We compare results of improved U-Net with a custom-designed U-Net architecture by analyzing the TCGA-LGG dataset (3929 images) from the TCI archive, and achieve pixel accuracies of 0.994 and 0.9975 from basic U-Net and improved U-Net architectures, respectively. Our results outperformed common CNN-based state-of-the-art works.

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Author notes

  1. Sourodip Ghosh and KC Santosh have contributed equally to the work.

Authors and Affiliations

  1. KC′s PAMI Research Lab − Computer Science, University of South Dakota, Vermillion, SD, 57069, USA

    Sourodip Ghosh & KC Santosh

  2. Department of Electronics Engineering, KIIT University, Bhubaneshwar, India

    Aunkit Chaki

Authors
  1. Sourodip Ghosh
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  2. Aunkit Chaki
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  3. KC Santosh
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Contributions

SG: Conceptualization, methodology and software; AC: Methodology and visualization; and KCS: Supervision, conceptualization, methodology, writing, reviewing and editing.

Corresponding author

Correspondence to KC Santosh.

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The authors declared that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Ghosh, S., Chaki, A. & Santosh, K. Improved U-Net architecture with VGG-16 for brain tumor segmentation. Phys Eng Sci Med 44, 703–712 (2021). https://doi.org/10.1007/s13246-021-01019-w

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  • DOI: https://doi.org/10.1007/s13246-021-01019-w

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