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Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder.

Hussah Nasser AlEisa1, Wajdi Touiti2, Amel Ali ALHussan1

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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This study introduces a novel breast cancer classification method using Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE). The approach achieves high accuracy in identifying malignant and benign breast masses from mammography images.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Accurate breast cancer classification is crucial for effective treatment.
  • Existing feature extraction methods for mammography analysis have limitations.
  • Developing robust automated systems can improve diagnostic efficiency.

Purpose of the Study:

  • To present a new breast cancer classification approach integrating Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE).
  • To enhance feature extraction from mammography images by focusing on relevant information.
  • To improve the accuracy and reliability of automated breast cancer detection.

Main Methods:

  • Utilizing FCNs for image segmentation to identify relevant zones in mammography images.
  • Employing BWAE for modeling extracted information, focusing on superior feature extraction capabilities.
  • Combining FCNs and BWAE to refine feature extraction by retaining only pertinent features for mass identification.

Main Results:

  • The proposed method demonstrated high effectiveness on a standard mammographic image dataset.
  • Achieved a precision of 94% for benign and 93% for malignant cases.
  • Reached a recall rate of 92% for benign and 95% for malignant cases, with 100% accuracy for normal cases.

Conclusions:

  • The fusion of FCNs and BWAE significantly improves breast cancer classification accuracy.
  • The proposed method shows competitive performance compared to state-of-the-art approaches.
  • This technique offers a promising tool for automated analysis of mammography images.