A streamlined U-Net convolution network for medical image processing
LUNeXt facilitates efficient medical image segmentation on standard hardware, reducing the learning curve and making advanced techniques more accessible to practitioners. This model balances the complexity and parameter count, offering a promising solution for enhancing the accuracy of pathological feature extraction in medical images.
Authors
- Ching‐Hsue Cheng (ORCID: https://orcid.org/0000-0002-5509-6965)
- Jun-He Yang (ORCID: https://orcid.org/0000-0002-4008-4785)
- Yu-Chen Hsu (ORCID: https://orcid.org/0009-0009-9022-1918)
Institutions
- National Yunlin University of Science and Technology (TW)
- Cheng Shiu University (TW)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-31
- DOI
- https://doi.org/10.21037/qims-24-1429
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00