Deep learning algorithms for the early detection of breast cancer - A comparative study with traditional machine learning

Aug 5, 2026

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Deep learning algorithms are assessed for early breast cancer detection by combining heterogeneous non-imaging data and comparing their performance with traditional machine learning. The approach uses feature selection on a dataset including 64 women diagnosed with breast cancer and a counterfactual group of 52 healthy women to identify the strongest prescreening predictors. These predictors are evaluated through k-fold Monte Carlo cross-validation. Results show a fine-tuned deep learning architecture with the lowest false negative rate and reduced prediction uncertainty versus machine learning, supporting more accurate, earlier, and non-invasive prescreening.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Breast cancer burden and screening context

Methods

Data sources and feature selection

Model comparison and validation

Results and Findings

False negative rate and Type II errors

Prediction uncertainty and performance metrics

Discussion

Clinical implications and benefits of prescreening

Conclusion

University of Groningen Deep learning algorithms for the early detection of breast cancer Gonzales Martinez, Rolando; van Dongen, Daan Max Published in: Informatics in Medicine Unlocked DOI: 10.1016/j.imu.2023.101317 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2023 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Gonzales Martinez, R. , & van Dongen, D. M. (2023) . Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning. Informatics in Medicine Unlocked, 41, Article 101317. [https://doi.org/10.1016/j.imu.2023.101317](https://doi.org/10.1016/j.imu.2023.101317) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 03-08-2026 Informatics in Medicine Unlocked 41 (2023) 101317 Contents lists available at ScienceDirect Informatics in Medicine Unlocked journal [homepage:](homepage: www.elsevier.com/locate/imu)[ www.elsevier.com/locate/imu](homepage: www.elsevier.com/locate/imu) | Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning | | | | | --- | --- | --- | --- | | Rolando Gonzales Martinez a, *, Daan-Max van Dongen b
a Royal Netherlands Academy of Arts and Sciences (KNAW), Netherlands Interdisciplinary Demographic Institute (NIDI), University of Groningen (RUG), the Netherlands
b University College London, United Kingdom | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Deep learning Machine learning Breast cancer Screening
Pre-screening False detection | | Deep learning has been widely applied in breast cancer screening to analyze images obtained from X-rays, ultrasound, magnetic resonances, and biopsies. This study suggests that deep learning can also be used to prescreen for cancer by analyzing heterogeneous data obtained from demographic and anthropometric information of patients, biological markers from routine blood samples, and relative risks from meta-analysis and international databases. In this document, feature selection is applied to a database of 64 women diagnosed with breast cancer and a counterfactual group of 52 healthy women, to identify the best predictors of cancer prescreening. The best predictors are used in k-fold Monte Carlo cross-validation experiments that compare deep learning against machine learning. The results indicate that a deep learning architecture that is fine-tuned using feature selection has the lowest false negative rate (i.e., the lowest Type II errors) and can effec

FAQs

How does the study use non-imaging information for breast cancer prescreening?

It applies feature selection to demographic and anthropometric data, routine blood biomarkers, and relative risks from meta-analyses and databases to identify the best predictors for prescreening.

What comparison method is used to evaluate deep learning versus traditional machine learning?

The best predictors are evaluated using k-fold Monte Carlo cross-validation, comparing deep learning architectures against machine learning approaches.

What performance advantage does deep learning show in this work?

A fine-tuned deep learning architecture achieves the lowest false negative rate (lowest Type II errors) and shows reduced prediction uncertainty, indicated by the lowest standard deviation of performance metrics.

Aug 5, 2026

Upload by Patrick