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Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.

Nadiah A Baghdadi1, Amer Malki2, Hossam Magdy Balaha3

  • 1College of Nursing, Nursing Management and Education Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Peerj. Computer Science
|September 12, 2022
PubMed
Summary

This study introduces an AI framework using convolutional neural networks (CNNs) and Manta Ray Foraging Optimization (MRFO) for accurate breast cancer classification from histological and ultrasound images, improving early detection.

Keywords:
Breast cancerConvolutional neural network (CNN)Deep learning (DL)Manta-Ray foraging algorithm (MRFO)Metaheuristic optimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a prevalent and dangerous disease, necessitating early detection for improved survival rates.
  • Manual diagnosis of medical images is challenging, time-consuming, and prone to errors.
  • Artificial intelligence, particularly deep learning, shows promise in enhancing the speed and reliability of breast cancer diagnosis.

Purpose of the Study:

  • To propose an automated and reliable framework for breast cancer classification using histological and ultrasound data.
  • To leverage convolutional neural networks (CNNs) combined with transfer learning and metaheuristic optimization for enhanced diagnostic performance.
  • To improve the adaptability and performance of CNN models through hyperparameter optimization using the Manta Ray Foraging Optimization (MRFO) algorithm.

Main Methods:

  • The study employed a framework built on CNNs, incorporating transfer learning with eight modern pre-trained architectures.
  • The Manta Ray Foraging Optimization (MRFO) algorithm was utilized to optimize the hyperparameters of the CNN models.
  • The framework was evaluated using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes).

Main Results:

  • The proposed framework achieved high accuracy, scoring 97.73% on histopathological data and 99.01% on ultrasound data.
  • Performance was assessed using various metrics including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity.
  • The experimental results demonstrated the superiority of the proposed framework compared to existing state-of-the-art methods.

Conclusions:

  • The developed AI framework offers an effective solution for automatic and reliable breast cancer classification.
  • The integration of CNNs, transfer learning, and MRFO optimization significantly enhances diagnostic accuracy in medical imaging.
  • This approach holds potential for improving early detection and aiding radiologists in breast cancer diagnosis.