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Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer

Amjad Rehman1, Tariq Mahmood1,2, Faten S Alamri3

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A novel deep learning method enhances breast cancer detection from microscopic images using low-dimensional features and transfer learning. This approach improves diagnostic accuracy and efficiency for better patient outcomes.

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Area of Science:

  • Digital pathology
  • Medical imaging analysis
  • Computational biology

Background:

  • Early breast cancer detection is vital for survival.
  • Current imaging methods have limitations.
  • Digital pathology and AI can improve accuracy.

Purpose of the Study:

  • To develop an accurate method for classifying benign and malignant breast cancer lesions from microscopic images.
  • To address challenges in feature extraction and computational complexity.

Main Methods:

  • A low-dimensional, multiple-channel feature-based approach using RGB channels.
  • Feature extraction via co-occurrence matrix, wavelet, Gabor, and histogram of oriented gradients.
  • The SqE-DDConvNet algorithm with transfer learning (mVVGNet16, EfficientNetV2B3, ResNet101V2, CN2XNet).

Main Results:

  • The proposed method achieved higher accuracy than baseline models.
  • Transfer learning preserved spatial information and improved accuracy across magnifications.
  • Enhanced recognition accuracy and training efficiency were observed.

Conclusions:

  • The deep learning methodology offers more accurate image classification for breast cancer microscopic images.
  • This approach contributes to improved diagnostic efficiency and patient care.
  • The study validates the efficacy of transfer learning in microscopic image analysis.