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Improved Bald Eagle Search Optimization with Synergic Deep Learning-Based Classification on Breast Cancer Imaging.

Manar Ahmed Hamza1, Hanan Abdullah Mengash2, Mohamed K Nour3

  • 1Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16242, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an improved bald eagle search optimization with synergic deep learning (IBESSDL-BCHI) for accurate breast cancer (BC) classification from histopathology images. The model enhances early diagnosis, crucial for improving patient outcomes in breast cancer detection.

Keywords:
bald eagle search algorithmbreast cancercomputer-aided diagnosisdeep learninghistopathological imagesmedical imaging

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Area of Science:

  • Medical imaging
  • Computational pathology
  • Artificial intelligence in healthcare

Background:

  • Breast cancer (BC) is a leading cause of mortality in women globally.
  • Histopathology image analysis is the gold standard for BC diagnosis but is labor-intensive and prone to human error.
  • Computer-aided diagnosis (CAD) systems offer potential for accurate and timely BC detection.

Purpose of the Study:

  • To develop and validate an improved bald eagle search optimization with a synergic deep learning mechanism for breast cancer diagnosis using histopathological images (IBESSDL-BCHI).
  • To enhance the accuracy and efficiency of breast cancer classification, aiding in earlier and more reliable diagnoses.

Main Methods:

  • Image preprocessing using median filtering (MF).
  • Feature extraction via a synergic deep learning (SDL) model.
  • Hyperparameter optimization of the SDL model using the improved bald eagle search (IBES) algorithm.
  • Classification of histopathology images into benign and malignant categories using Long Short-Term Memory (LSTM).

Main Results:

  • The proposed IBESSDL-BCHI model demonstrated strong performance in classifying breast cancer from histopathology images.
  • The system achieved better general efficiency for breast cancer classification compared to existing methods on a benchmark dataset.

Conclusions:

  • The IBESSDL-BCHI model offers a promising approach for accurate and efficient breast cancer diagnosis using deep learning and optimization techniques.
  • This AI-driven method can assist pathologists, reduce diagnostic errors, and facilitate timely treatment initiation for breast cancer patients.