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Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.

Mariam Busaleh1, Muhammad Hussain1, Hatim A Aboalsamh1

  • 1Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia.

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|November 25, 2021
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Summary
This summary is machine-generated.

Accurate breast cancer mass classification is crucial. A new computer-aided system using diverse contextual information and an ensemble classifier significantly improves the detection of malignant masses, aiding radiologists.

Keywords:
BIRADSbreast mass classificationconvolutional neural network (CNN)ensemble classifiermammographytransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Accurate classification of breast masses as benign or malignant is vital for improving breast cancer survival rates.
  • Similar texture patterns between benign and malignant masses present a diagnostic challenge, leading to low sensitivity and specificity in current methods.
  • Diverse contextual information surrounding a mass region can be a strong indicator for differentiating between benign and malignant cases.

Purpose of the Study:

  • To introduce a novel computer-aided system for enhanced classification of breast masses.
  • To leverage diverse contextual information and ensemble classification for improved diagnostic accuracy.
  • To outperform existing methods in distinguishing benign from malignant breast masses.

Main Methods:

  • The system utilizes multiple regions of interest (ROIs) to model diverse contextual information around a mass.
  • A single ResNet-50 model serves as the backbone for local decision-making, enhanced by a data augmentation technique.
  • Stacking with a Support Vector Machine (SVM) model integrates local decisions for the final prediction.

Main Results:

  • The system achieved a high sensitivity of 98.48% and a specificity of 92.31% on the CBIS-DDSM dataset.
  • Performance was further enhanced when the system was trained and tested on data specific to a particular breast density BI-RADS class.
  • The proposed method demonstrated superior performance compared to state-of-the-art techniques in classifying mass regions.

Conclusions:

  • The developed system effectively classifies breast masses by incorporating diverse contextual information through multiple ROIs, avoiding the need for multiple CNN model fine-tuning.
  • This approach offers a significant advancement in computer-aided diagnosis for breast cancer, outperforming existing methods.
  • The system has the potential to reduce radiologist workload and improve the sensitivity of malignant mass prediction.