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Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature

Meenakshi Devi P1, Muna A2, Yasser Ali3

  • 1Department of Information Technology, K.S.R. College of Engineering, Tiruchengode, Tamilnadu, 637215, India.

BMC Medical Imaging
|January 8, 2025
PubMed
Summary

A novel deep learning model, BCDNet, accurately classifies breast tumors from ultrasound images, achieving 94.5% accuracy. This advancement aids in early breast cancer detection, potentially saving lives.

Keywords:
Atrous spatial pyramid poolingBreast cancer diagnosis modelBreast cancer diagnosis networkRandom parameterized adaptive opposition slime mould with egret swarm optimizationUltrasound images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of death in women, necessitating early detection for improved survival.
  • Manual diagnosis is time-consuming and subjective; existing CAD models struggle with ultrasound image variability.
  • Ultrasound is a cost-effective, portable, and non-invasive imaging modality for breast cancer screening.

Purpose of the Study:

  • To develop an effective deep learning model (BCDNet) for accurate breast tumor classification (benign vs. malignant) using ultrasound images.
  • To optimize model performance using the RPAOSM-ESO algorithm for enhanced accuracy and reduced false negatives.
  • To facilitate early-stage breast cancer detection and reduce mortality rates.

Main Methods:

  • The BCDNet model employs transfer learning from VGG16 for feature extraction.
  • It incorporates an AHDNAM classification approach: ASPP, DTCN, 1DCNN, and an attention mechanism.
  • The RPAOSM-ESO algorithm fine-tunes model weights and parameters.

Main Results:

  • The RPAOSM-ESO-BCDNet model achieved an accuracy of 94.5%.
  • This performance surpasses previous models like DTCN (88.2%), 1DCNN (89.6%), MobileNet (91.3%), and ASPP-DTC-1DCNN-AM (93.8%).
  • The model demonstrates superior accuracy in classifying breast tumors from ultrasound images.

Conclusions:

  • The BCDNet model, optimized by RPAOSM-ESO, shows significant promise for accurate breast tumor classification using ultrasound.
  • It can serve as a valuable tool for early breast cancer detection.
  • The model has the potential to improve patient outcomes and reduce healthcare burdens.