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Parallel convolutional SpinalNet: A hybrid deep learning approach for breast cancer detection using mammogram images.

Vinay Gautam1, Anu Saini2, Alok Misra3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India.

Network (Bristol, England)
|March 24, 2025
PubMed
Summary

This study introduces a novel Parallel Convolutional SpinalNet (PConv-SpinalNet) for early breast cancer detection from mammograms. The PConv-SpinalNet model achieved high accuracy, improving diagnostic capabilities for this critical disease.

Keywords:
Breast cancerLadderNetSpinalNetmammogram imageparallel convolutional neural network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer remains a leading cause of mortality in women, emphasizing the need for early and accurate diagnostic methods.
  • Timely detection significantly improves patient outcomes and survival rates, making advanced imaging analysis crucial.

Purpose of the Study:

  • To propose and evaluate a novel deep learning model, Parallel Convolutional SpinalNet (PConv-SpinalNet), for efficient and accurate breast cancer detection using mammogram images.
  • To enhance the diagnostic performance by integrating various image processing and feature extraction techniques.

Main Methods:

  • Mammogram images were pre-processed using Gabor filters and tumors were segmented with LadderNet.
  • Advanced augmentation techniques (Image manipulation, erasing, mix) were applied to segmented samples.
  • A comprehensive feature extraction phase included CNN features, Texton, Local Gabor Binary Patterns (LGBP), Scale-Invariant Feature Transform (SIFT), and Local Monotonic Pattern (LMP) with Discrete Cosine Transform (DCT).
  • The PConv-SpinalNet, combining Parallel Convolutional Neural Networks (PCNN) and SpinalNet, was utilized for final detection.

Main Results:

  • The PConv-SpinalNet model demonstrated a high accuracy of 88.5%.
  • Key performance metrics included a True Positive Rate (TPR) of 89.7%, True Negative Rate (TNR) of 90.7%, Positive Predictive Value (PPV) of 91.3%, and Negative Predictive Value (NPV) of 92.5%.

Conclusions:

  • The proposed PConv-SpinalNet model shows significant potential for improving the accuracy and efficiency of breast cancer detection in mammography.
  • This deep learning approach offers a promising tool for early diagnosis, potentially reducing mortality rates and enhancing patient care.