# Pre-screening breast cancer with machine learning and deep learning

arXiv: https://arxiv.org/abs/2302.02406 · HTML: https://mlsanity.com/paper/2302.02406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 62 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Deep learning for biomedical photoacoustic imaging: A review](https://mlsanity.com/paper/2011.02744/) (2020)

## Abstract (arXiv)

We suggest that deep learning can be used for pre-screening cancer by analyzing demographic and anthropometric information of patients, as well as biological markers obtained from routine blood samples and relative risks obtained from meta-analysis and international databases. We applied feature selection algorithms to a database of 116 women, including 52 healthy women and 64 women diagnosed with breast cancer, to identify the best pre-screening predictors of cancer. We utilized the best predictors to perform k-fold Monte Carlo cross-validation experiments that compare deep learning against traditional machine learning algorithms. Our results indicate that a deep learning model with an input-layer architecture that is fine-tuned using feature selection can effectively distinguish between patients with and without cancer. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions. These findings suggest that deep learning algorithms applied to cancer pre-screening offer a radiation-free, non-invasive, and affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer pre-screening offer opportunities to identify individuals who may require imaging-based screening, can encourage self-examination, and decrease the psychological externalities associated with false positives in cancer screening. The integration of deep learning algorithms for both screening and pre-screening will ultimately lead to earlier detection of malignancy, reducing the healthcare and societal burden associated to cancer treatment.