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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

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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 cancer Convolutional neural network InceptionV3 Mammography Visual 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.