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Updated: Jan 13, 2026

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Deep learning-based bacterial foraging optimization algorithm to improve digital mammography-based breast cancer

D Banumathy1, D Karthikeyan2, G Mohanraj3

  • 1Paavai Engineering College, Pachal, Namakkal, 637018, India. banumathydhanabalanpec@paavai.edu.in.

Scientific Reports
|October 30, 2025
PubMed
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This study enhances early breast cancer detection using deep learning and automatic hyperparameter optimization. The Bacterial Foraging Optimization-Convolutional Neural Network (BFO-CNN) model significantly improves mammogram analysis accuracy.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Mammography is crucial for early breast cancer detection but relies on subjective analysis, leading to potential inaccuracies.
  • Deep learning models, particularly Convolutional Neural Networks (CNNs), show promise in image analysis tasks like mammogram interpretation.
  • Manual hyperparameter tuning for CNNs is time-consuming and labor-intensive, hindering optimal performance.

Purpose of the Study:

  • To improve the accuracy and efficiency of early breast cancer detection using deep learning techniques.
  • To explore the application of various computer vision models, including VGG19, InceptionV3, and a custom CNN, for mammogram analysis.
  • To implement automatic hyperparameter optimization for CNNs to enhance their diagnostic capabilities.

Main Methods:

Keywords:
Bacterial foraging optimization.Breast cancerConvolutional neural networkInceptionV3MammographyVisual geometry group 19

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  • Utilized the Digital Database for Screening Mammography (DDSM) dataset for mammographic image analysis.
  • Investigated deep learning models: Visual Geometry Group (VGG) 19, Inception V3, and a custom 20-layer Convolutional Neural Network (CNN).
  • Employed the Bacterial Foraging Optimization (BFO) algorithm for automatic hyperparameter optimization of CNNs, including filter size, number of filters, and hidden layers.

Main Results:

  • The proposed Bacterial Foraging Optimization-Convolutional Neural Network (BFO-CNN) method demonstrated superior performance over existing methods.
  • Achieved accuracy improvements of 7.62% for VGG 19, 9.16% for InceptionV3, and 1.78% for the custom CNN-20 model.
  • Automatic hyperparameter optimization using BFO significantly boosted CNN model efficacy in breast cancer detection.

Conclusions:

  • Deep learning and automatic hyperparameter optimization offer a powerful approach to enhance breast cancer detection through mammogram analysis.
  • The BFO-CNN model presents significant potential for improving breast cancer diagnosis accuracy compared to conventional CNN architectures.
  • This research highlights the value of metaheuristic algorithms in optimizing deep learning models for medical image analysis.