MorphoNet: An Interpretable Hierarchical Deep Learning Framework for Multi-Class Skin Lesion Classification Using Dermoscopic Morphology
- DOI
- 10.3390/bioengineering13090989
- Published
- 2026-08-27
- Container
- Bioengineering
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/bioengineering13090989,
title = {MorphoNet: An Interpretable Hierarchical Deep Learning Framework for Multi-Class Skin Lesion Classification Using Dermoscopic Morphology},
author = {Bradley Yao and Angela Jin and Huijuan Liu and Qiliang Li},
year = {2026},
journal = {Bioengineering},
doi = {10.3390/bioengineering13090989},
url = {https://doi.org/10.3390/bioengineering13090989}
}RIS
TY - JOUR TI - MorphoNet: An Interpretable Hierarchical Deep Learning Framework for Multi-Class Skin Lesion Classification Using Dermoscopic Morphology AU - Bradley Yao AU - Angela Jin AU - Huijuan Liu AU - Qiliang Li PY - 2026 JO - Bioengineering DO - 10.3390/bioengineering13090989 UR - https://doi.org/10.3390/bioengineering13090989 ER -
APA
Yao, B., Jin, A., Liu, H., & Li, Q. (2026). MorphoNet: An Interpretable Hierarchical Deep Learning Framework for Multi-Class Skin Lesion Classification Using Dermoscopic Morphology. Bioengineering. https://doi.org/10.3390/bioengineering13090989
Source records
- crossref · retrieved 2026-09-26T17:27:45.064Z