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BCD-TransNet: Automatic breast cancer detection and classification using transfer learning approach.

Amanullakhan M1, Sridhar P2, Indra J3

  • 1Department of Electronics and Communication Engineering, Mohamed Sathak Engineering College, Kilakarai, India.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|May 7, 2025
PubMed
Summary

A novel transfer learning framework, BCD-TransNet, accurately classifies breast cancer (BC) stages from histopathological images. This method enhances early diagnosis by improving classification performance over existing techniques.

Keywords:
Greedy snake optimizationKrill Herd optimization algorithmbreast cancerdecision treestationary wavelet based Retinex

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Area of Science:

  • Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Breast cancer (BC) is a leading cause of death in women, with early diagnosis crucial for effective treatment.
  • Current machine learning (ML) techniques face challenges in accurately classifying BC and aiding early diagnosis.
  • Transfer learning shows promise for improving BC classification using histopathological images.

Purpose of the Study:

  • To introduce a novel Breast Cancer Detection using Transfer learning network (BCD-TransNet) for identifying and classifying BC stages.
  • To enhance the accuracy and efficiency of BC diagnosis through an advanced transfer learning framework.
  • To overcome limitations in existing ML techniques for BC classification and early detection.

Main Methods:

  • Histopathological images from the BreakHis dataset were pre-processed using stationary wavelet based Retinex (SWR) for noise reduction and quality enhancement.
  • Image segmentation was performed using the Hybrid Greedy Snake-Krill Herd Optimization (HGS-KHO) algorithm.
  • The BCD-TransNet model utilized five pre-trained networks for feature extraction, followed by a two-level classification and ML-based Decision Tree for staging.

Main Results:

  • The BCD-TransNet model achieved a high accuracy of 99.31% in classifying breast tumors.
  • The proposed transfer learning model demonstrated superior performance compared to DLA-EABA, Pa-DBN-BC, and TTCNN, with accuracy improvements of 2.11%, 13.31%, and 1.82%, respectively.
  • The model effectively performed a two-level classification, distinguishing between benign and malignant cells and their subtypes.

Conclusions:

  • The BCD-TransNet model offers a significant advancement in the accurate classification and staging of breast cancer.
  • This transfer learning-based approach provides a robust solution for improving early breast cancer diagnosis.
  • The proposed method demonstrates superior performance, highlighting its potential for clinical application in breast cancer detection.