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Related Experiment Video

Updated: Jan 17, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line

Xiang Zhang1, Wei Shao2, Ming Qiu3

  • 1Department of Information Management Center, Zhongshan Hospital Affiliated to Dalian University, Dalian, Liaoning, China.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary

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Machine learning models, including deep neural networks and convolutional neural networks, show high accuracy in diagnosing breast cancer from the Wisconsin Breast Cancer Dataset, indicating strong clinical applicability.

Area of Science:

  • Medical Informatics
  • Machine Learning
  • Biomedical Data Science

Background:

  • Accurate breast cancer diagnosis is crucial for effective treatment and patient outcomes.
  • Machine learning offers powerful tools for analyzing complex medical datasets.
  • The Wisconsin Breast Cancer Dataset (WBCD) is a widely used resource for evaluating diagnostic models.

Purpose of the Study:

  • To evaluate the performance of various machine learning models for breast cancer diagnosis.
  • To compare the diagnostic accuracy of Random Forest, XGBoost, Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models.
  • To assess the clinical applicability of optimized machine learning models for breast cancer detection.

Main Methods:

  • The Wisconsin Breast Cancer Dataset (WBCD) was preprocessed, addressing missing values and duplicates.
Keywords:
Breast cancer diagnosisDeep neural networks (DNN)Machine learningMulti-line classifiersRandom forestSupervised learningTransfer learningWisconsin Breast Cancer DatabaseXGBoost

Related Experiment Videos

Last Updated: Jan 17, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
  • Data were split into training (80%) and testing (20%) sets, maintaining class distribution.
  • 10-fold cross-validation was employed to evaluate Random Forest, XGBoost, and DNN models.
  • Bayesian hyperparameter tuning was used to optimize the DNN model.
  • Transfer learning with VGG16 was applied to develop a CNN model.

    • Main Results:

    • Deep Neural Network (DNN) achieved 98.0% accuracy, improving to 98.9% after hyperparameter tuning.
    • Convolutional Neural Network (CNN) using VGG16 transfer learning reached 99.3% accuracy.
    • Both optimized DNN and CNN models demonstrated high precision, recall, and ROC-AUC scores.
    • The models showed excellent performance in distinguishing between benign and malignant breast cancer instances.

    Conclusions:

    • Optimized Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models exhibit high diagnostic accuracy for breast cancer.
    • These machine learning models demonstrate reliable performance and promising clinical applicability for breast cancer diagnosis.
    • Further validation is warranted, particularly for the CNN model due to potential domain mismatch issues.