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Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling.

Refat Khan Pathan1, Fahim Irfan Alam2, Suraiya Yasmin3

  • 1Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Selangor, Malaysia.

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Summary
This summary is machine-generated.

Artificial intelligence rapidly detects breast cancer using ultrasound images. A multi-headed convolutional neural network (CNN) achieved 92.31% accuracy, improving diagnosis and reducing human error.

Keywords:
breast cancer classificationmedical image modelingmulti-headed CNNultrasound image processing

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a prevalent disease, necessitating accurate and efficient diagnostic tools.
  • Traditional analytical techniques for breast cancer detection include MRI, X-ray, mammography, and ultrasound.
  • The Breast Ultrasound Images Dataset (BUSI) provides a valuable resource for developing automated detection systems.

Purpose of the Study:

  • To develop and validate an artificial intelligence framework for the rapid detection of breast cancer using ultrasound images.
  • To evaluate the performance of a multi-headed convolutional neural network (CNN) on both raw and masked ultrasound images.
  • To assess the contribution of combining different data inputs for improved diagnostic accuracy.

Main Methods:

  • Utilized the Breast Ultrasound Images Dataset (BUSI), comprising benign, malignant, and normal ultrasound images.
  • Employed a multi-headed convolutional neural network (CNN) architecture for image analysis.
  • Performed validation using quantitative performance measures and Mean Squared Error (MSE) loss calculation.

Main Results:

  • The framework achieved 78.97% accuracy with raw images and 81.02% with masked images.
  • Combining raw and masked images with a multi-headed CNN significantly improved accuracy to 92.31% (±2).
  • The model demonstrated a low Mean Squared Error (MSE) loss of 0.05, indicating effective learning.

Conclusions:

  • The multi-headed CNN framework is effective for breast cancer detection from ultrasound images.
  • Utilizing both raw and masked image data enhances diagnostic performance.
  • The developed system, accessible via a web interface, has the potential to reduce human error in clinical diagnosis.