High-precision identification of breast cancer based on end-to-end parallel spectral convolutional neural network assisted laser-induced breakdown spectroscopy
A parallel spectral CNN (PSCNN) integrates spectral preprocessing and breast cancer identification in a multitask framework, enhancing feature learning and classification performance through effective information sharing and regularization.
Authors
- Shengqun Shi (ORCID: https://orcid.org/0000-0001-7988-5267)
- Li-Li Peng (ORCID: https://orcid.org/0000-0002-3432-1895)
- Lianbo Guo (ORCID: https://orcid.org/0000-0003-1038-8443)
- Lingling Pi
- Yuanchao Liu (ORCID: https://orcid.org/0000-0001-7699-488X)
- Xiong Wang
- Honghua Ma
- Deng Zhang (ORCID: https://orcid.org/0000-0002-4120-5032)
- Nan Deng (ORCID: https://orcid.org/0000-0003-0914-7220)
Institutions
- Nanjing Normal University (CN)
- City University of Hong Kong (HK)
- Wuhan National Laboratory for Optoelectronics (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4ja00238e
- Citations
- 6
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 4.51