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Breast Cancer Image Classification: A Review.

Pooja Pathak1, Anand Singh Jalal2, Ritu Rai2

  • 1Department of Mathematics, GLA University, Mathura, India.

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Summary

This review explores Computer-Aided Diagnosis (CAD) systems for breast cancer detection, focusing on conventional and artificial intelligence (AI) approaches to improve early diagnosis and treatment outcomes.

Keywords:
Breast cancerComputer-Aided Diagnosis (CAD)artificial intelligenceimage classification.medical imagingtumour

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Breast cancer is the most common cancer in women globally, necessitating early detection for improved survival and treatment.
  • Magnetic Resonance Imaging (MRI) is a valuable screening tool for early breast cancer detection, but requires Computer-Aided Diagnosis (CAD) due to large data volumes.
  • Computer-Aided Diagnosis (CAD) systems are crucial for analyzing complex medical imaging data in breast cancer screening.

Purpose of the Study:

  • To review the various approaches utilized in Computer-Aided Diagnosis (CAD) systems for breast cancer detection.
  • To provide a comprehensive overview of methods for breast cancer diagnosis using CAD.
  • To highlight the importance of CAD in enhancing the interpretation of breast cancer screening images.

Main Methods:

  • Categorization of CAD methods into two main classes: conventional and artificial intelligence (AI) approaches.
  • Detailed examination of the conventional approach, including image preprocessing, segmentation, feature extraction, and classification.
  • Exploration of AI approaches, specifically focusing on convolutional and deep learning networks for diagnostic purposes.

Main Results:

  • Conventional CAD methods involve fundamental image processing techniques for breast cancer analysis.
  • Artificial intelligence (AI) methods, particularly deep learning, offer advanced capabilities for breast cancer diagnosis from medical images.
  • Both conventional and AI approaches contribute to the development of effective CAD systems for breast cancer detection.

Conclusions:

  • This review synthesizes core concepts in breast cancer diagnosis and CAD systems.
  • It presents a comprehensive overview of past research and methodologies in breast cancer CAD.
  • The findings underscore the evolving role of CAD, especially AI-driven systems, in improving breast cancer detection and patient outcomes.