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A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.

Anas Bilal1,2, Ali Alkhathlan3, Faris A Kateb4

  • 1College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.

Scientific Reports
|January 25, 2025
PubMed
Summary

A new hybrid approach, Q-BGWO-SQSVM, enhances early breast cancer detection using optimized mammography image analysis. This accurate and sensitive computer-aided diagnosis system shows promising results for improved healthcare outcomes.

Keywords:
Breast cancerGrey wolf optimizationMedical image analysisQuantum computingSupport vector machine

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Early breast cancer detection is critical for reducing mortality and improving treatment efficacy.
  • Current computer-aided diagnosis (CAD) systems for mammography face challenges like overfitting and data dependency.
  • There is a need for more reliable and adaptable CAD systems for accurate breast cancer classification.

Purpose of the Study:

  • To introduce a novel hybrid approach, Q-BGWO-SQSVM, for enhancing breast cancer classification accuracy in mammography.
  • To address limitations of existing CAD systems by improving adaptability and reducing reliance on massive annotated datasets.
  • To develop a more sensitive and reliable CAD system for early breast cancer detection.

Main Methods:

  • A hybrid approach combining SqueezeNet for feature extraction and an improved quantum-inspired binary Grey Wolf Optimizer (Q-BGWO) for optimizing Support Vector Machine (SVM) parameters.
  • Utilizing SqueezeNet's fire modules and bypass mechanisms to extract distinct features from mammography images.
  • Optimizing SVM parameters via Q-BGWO for sophisticated performance in breast cancer classification.

Main Results:

  • The Q-BGWO-SQSVM model demonstrated excellent performance across multiple datasets (MIAS, INbreast, DDSM, CBIS-DDSM).
  • Achieved high accuracy (99%), sensitivity (98%), and specificity (100%) on the CBIS-DDSM dataset using 15-fold cross-validation.
  • Outperformed state-of-the-art classification methods in terms of accuracy, sensitivity, specificity, precision, F1 score, and MCC.

Conclusions:

  • The proposed Q-BGWO-SQSVM model offers a reliable and accurate solution for early breast cancer detection.
  • The system's sophisticated performance and adaptability show potential for broader application in diverse imaging conditions.
  • This advancement in CAD systems is crucial for further development in breast cancer diagnostics and healthcare.