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U-Net Based Efficient Deep Learning Architecture for Effective Skin Lesion Segmentation

https://doi.org/10.2139/ssrn.4185484
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9/9 checkable references clean · checked 2026-08-29

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

14 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 9 checked references that resolve
resolves10.1049/ipr2.12419
Medical image segmentation using deep learning: A survey
resolves10.1109/TMI.2018.2791721
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
resolves10.1016/j.neunet.2019.08.025
MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation
resolves10.1016/j.media.2017.07.005
A survey on deep learning in medical image analysis
resolves10.1109/3DV.2016.79
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
resolves10.1109/TMI.2019.2948320
Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
resolves10.1109/ISBI.2019.8759555
Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation
resolves10.1007/978-3-642-24797-2_4
Long Short-Term Memory
resolves10.1007/978-3-319-67389-9_44
Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks
The 14 references without a DOI — listed, not checked
no DOI — not checkedref3
no DOI — not checkedref4
no DOI — not checkedReal-time automatic fetal brain extraction in fetal mri by deep learning
no DOI — not checkedFully convolutional networks for semantic segmentation
no DOI — not checkedPsp net-based automatic segmentation network model for prostate magnetic resonance imaging
no DOI — not checkedref8
no DOI — not checkedref9
no DOI — not checkedRonneberger, 3d u-net: learning dense volumetric segmentation from sparse annotation
no DOI — not checkedU-net: Convolutional networks for biomedical image segmentation
no DOI — not checkedref17
no DOI — not checkedFully convolutional structured lstm networks for joint 4d medical image segmentation
no DOI — not checkedRecurrent neural networks for aortic image sequence segmentation with sparse annotations
no DOI — not checkedDeep residual learning for image recognition
no DOI — not checkedOptimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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