Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.

Hasan AM, Al-Waely NKN, Alfalluji WL, Ibrahim RW, Jalab HA, Meziane F

DOI: 10.3390/jimaging12090415
Published: 2026 Sep 3
Container: Journal of imaging
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/jimaging12090415,
  title = {Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.},
  author = {Hasan AM and Al-Waely NKN and Alfalluji WL and Ibrahim RW and Jalab HA and Meziane F},
  year = {2026},
  journal = {Journal of imaging},
  doi = {10.3390/jimaging12090415},
  url = {https://doi.org/10.3390/jimaging12090415}
}

RIS

TY  - JOUR
TI  - Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.
AU  - Hasan AM
AU  - Al-Waely NKN
AU  - Alfalluji WL
AU  - Ibrahim RW
AU  - Jalab HA
AU  - Meziane F
PY  - 2026
JO  - Journal of imaging
DO  - 10.3390/jimaging12090415
UR  - https://doi.org/10.3390/jimaging12090415
ER  -

APA

AM, H., NKN, A., WL, A., RW, I., HA, J., & F, M. (2026). Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.. Journal of imaging. https://doi.org/10.3390/jimaging12090415

Source records

pubmed · retrieved 2026-11-10T08:20:14.434Z