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Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer

Marwa Obayya1, Mashael S Maashi2, Nadhem Nemri3

  • 1Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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|February 11, 2023
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Summary

This study introduces an AI-driven method for breast cancer classification from histopathological images. The AOADL-HBCC technique achieves high accuracy, improving diagnostic decision-making in healthcare.

Keywords:
breast cancer classificationdecision makingdeep learninghealthcarehistopathological images

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Histopathological images are crucial for breast cancer diagnosis.
  • Manual analysis is time-consuming and prone to errors.
  • Automated analysis using AI and deep learning (DL) offers significant potential.

Purpose of the Study:

  • To develop an advanced AI technique for accurate histopathological breast cancer classification.
  • To enhance diagnostic decision-making in healthcare through automated image analysis.

Main Methods:

  • Developed the Arithmetic Optimization Algorithm with Deep-Learning-based Histopathological Breast Cancer Classification (AOADL-HBCC) technique.
  • Implemented noise removal using median filtering (MF) and contrast enhancement.
  • Utilized SqueezeNet model with Arithmetic Optimization Algorithm (AOA) for feature extraction.
  • Employed a Deep Belief Network (DBN) classifier with Adamax optimizer for classification.

Main Results:

  • The AOADL-HBCC technique demonstrated superior performance compared to existing methods.
  • Achieved a maximum classification accuracy of 96.77% for breast cancer detection.
  • The integrated approach effectively handles image noise and enhances contrast.

Conclusions:

  • The AOADL-HBCC technique offers a highly accurate and efficient automated solution for breast cancer classification.
  • This AI-driven approach has the potential to significantly improve diagnostic accuracy and efficiency in clinical practice.