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Advanced deep learning framework for breast cancer detection using digital breast tomosynthesis images.

G Bharatha Sreeja1, S Sudha2, T M Inbamalar3

  • 1231516 ECE Department, Easwari Engineering College , Chennai, Tamil Nadu, India.

Biomedizinische Technik. Biomedical Engineering
|January 11, 2026
PubMed
Summary

This study introduces a deep learning framework for detecting breast cancer using digital breast tomosynthesis (DBT) images. The advanced model significantly improved diagnostic performance, offering a promising tool for early breast cancer detection.

Keywords:
convolutional neural network (CNN)digital breast tomosynthesis (DBT)exhaustive feature selection (EFS)extreme gradient boosting (XGBoost)hyperparameter tuninglaplacian pyramid enhancement (LPE)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early and accurate breast cancer detection is crucial for improving patient survival rates.
  • Digital Breast Tomosynthesis (DBT) offers advanced imaging capabilities for breast cancer diagnosis.

Purpose of the Study:

  • To develop a robust deep learning framework for breast cancer detection using DBT images.
  • To leverage both single-slice and multi-slice DBT inputs for enhanced detection performance.

Main Methods:

  • Image preprocessing including normalization, resizing, and Laplacian Pyramid Enhancement (LPE).
  • Feature extraction, fusion, and selection using Exhaustive Feature Selection (EFS).
  • A hybrid classification model combining ResNet V2, MobileNet V3, and Inception V3+, with XGBoost ensemble learning and hyperparameter optimization.

Main Results:

  • The hybrid model utilizing multi-slice DBT inputs demonstrated superior accuracy, sensitivity, specificity, and AUC.
  • Laplacian Pyramid Enhancement (LPE), feature fusion, and Exhaustive Feature Selection (EFS) substantially improved diagnostic performance.

Conclusions:

  • Advanced deep learning methods show significant potential for enhancing breast cancer detection performance.
  • Future work includes integration with clinical decision support systems, multi-center datasets, and other imaging modalities.