Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A step toward computer-assisted mammography using evolutionary programming and neural networks.

D B Fogel1, E C Wasson, E M Boughton

  • 1Natural Selection, Inc., 3333 N. Torrey Pines Ct., Suite 200, La Jolla, CA 92037, USA. dfogel@natural-selection.com

Cancer Letters
|April 1, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evolving neural networks to play checkers without relying on expert knowledge.

IEEE transactions on neural networks·2008
Same author

Multiple-vector self-adaptation in evolutionary algorithms.

Bio Systems·2001
Same author

Do evolutionary processes minimize expected losses?

Journal of theoretical biology·2000
Same author

Fitness distributions in evolutionary computation: motivation and examples in the continuous domain.

Bio Systems·2000
Same author

Rapid automated molecular replacement by evolutionary search.

Acta crystallographica. Section D, Biological crystallography·1999
Same author

Evolving artificial neural networks for screening features from mammograms.

Artificial intelligence in medicine·1998
Same journal

Corrigendum to "Elevated MMP10/13 mediated barrier disruption and NF-κB activation aggravate colitis and colon tumorigenesis in both individual or full miR-148/152 family knockout mice" [Cancer Lett. (2022) 31 529 53-69].

Cancer letters·2026
Same journal

Corrigendum to "PI3K PROTAC overcomes the lapatinib resistance in PIK3CA-mutant HER2 positive breast cancer" [Cancer Lett. 598 (2024) 217112].

Cancer letters·2026
Same journal

Differential bioenergetic reprogramming driven by sorafenib reveals therapeutic vulnerabilities in biliary tract cancers.

Cancer letters·2026
Same journal

Transcriptomic-based molecular classification of ampullary adenocarcinoma.

Cancer letters·2026
Same journal

Corrigendum to "LRH1 promotes pancreatic cancer metastasis" [Cancer Lett. (2014) 24 350 (1-2) 15-24].

Cancer letters·2026
Same journal

Corrigendum to "Histone deacetylase inhibitors upregulate Snail via Smad2/3phosphorylation and stabilization of Snail to promote metastasis of hepatoma cells" [Cancer Lett. 420 (2018) 1-13].

Cancer letters·2026
See all related articles

Artificial intelligence (AI) can enhance breast cancer detection by providing a second opinion. Evolutionary programming trained neural networks show promise in identifying malignancies from radiographic data.

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Oncology diagnostics

Background:

  • Artificial intelligence (AI) offers potential for improving diagnostic accuracy and cost-effectiveness in medicine.
  • AI can serve as a valuable tool for second opinions in clinical settings.
  • Early and accurate breast cancer detection is crucial for patient outcomes.

Purpose of the Study:

  • To investigate the efficacy of artificial neural networks (ANNs) trained with evolutionary programming for breast cancer detection.
  • To evaluate the use of radiographic features and patient age in AI-driven cancer diagnosis.
  • To assess the diagnostic performance of simple neural architectures in identifying malignant breast masses.

Main Methods:

  • Utilized evolutionary programming to train artificial neural networks (ANNs).

Related Experiment Videos

  • Employed radiographic features and patient age as input data for the models.
  • Analyzed data from 112 biopsy-proven breast masses (63 malignant, 49 benign).

    • Main Results:

    • The trained ANNs demonstrated a significant probability of detecting malignancies.
    • Simple neural architectures were sufficient for achieving high detection rates.
    • A small percentage of false positives were observed in the diagnostic results.

    Conclusions:

    • AI techniques, specifically evolutionary programming-trained ANNs, show potential for accurate breast cancer detection.
    • The study highlights the feasibility of using AI for improving diagnostic sensitivity and specificity in mammography.
    • Further research into refining AI models can minimize false positives and enhance clinical utility.