Interpretable patient-voting deep learning-enhanced Raman spectroscopy of serum for breast cancer detection
Deep learning detected breast cancer with 95% accuracy using blood analysis, with an explainable framework that identifies specific markers.
A new deep learning approach to serum Raman spectroscopy detected breast cancer with 95% accuracy across 732 subjects, identifying tryptophan and phenylalanine as key diagnostic markers. The interpretable AI framework addresses the 'black box' problem in spectroscopy-based diagnostics.
What the study was
- Study design
- Diagnostic validation study
- Population
- Breast cancer patients and healthy controls
- Sample size
- 732
- Category
- Early Detection
- Maturity
- Exploratory
- Journal
- Spectrochimica Acta Part A
Why it surfaced
Strong diagnostic performance with interpretability; needs external validation and prospective testing.
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