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ZENODO
Software . 2026
License: http://opensource.org/licenses/MIT
Data sources: ZENODO
ZENODO
Software . 2026
License: https://opensource.org/licenses/MIT
Data sources: Datacite
Claim

EfficientNet-UNet-Brain-Tumor-Segmentation-TVC

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 25 Jan 2026Publisher:Zenodo
Authors: Aleena Shafiq, Aleena;
Code repository: https://github.com/Aleena-Shafiq/EfficientNet-UNet-Brain-Tumor-Segmentation-TVC/tree/0.1.1

doi: 10.5281/zenodo.18365844

EfficientNet-UNet-Brain-Tumor-Segmentation-TVC

Abstract

Implements an EfficientNet-B0 encoder with a U-Net decoder for whole brain tumor MRI segmentation. The full training/testing pipeline runs in one Jupyter Notebook on BraTS2020, focusing on accuracy, efficiency, reproducibility, and explainability using Grad-CAM.

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average