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Applying Deep Learning for Breast Cancer Detection in Radiology.

Ella Mahoro1, Moulay A Akhloufi1

  • 1Perception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada.

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Summary
This summary is machine-generated.

Deep learning enhances breast cancer screening by analyzing medical images from mammography, thermography, and MRI. This review explores AI integration in clinical practice, highlighting limitations and opportunities for early detection.

Keywords:
breast cancerclassificationconvolutional neural networkdeep learningdetectionradiologysegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of death among women globally.
  • Early detection significantly improves patient outcomes and survival rates.
  • Deep learning (DL) shows promise in advancing medical imaging analysis.

Purpose of the Study:

  • To review the application of deep learning methods in breast cancer screening workflows.
  • To summarize DL techniques, data considerations, and various imaging modalities used.
  • To explore the integration of artificial intelligence (AI) in diagnostic breast imaging.

Main Methods:

  • Literature review of deep learning applications in breast cancer screening.
  • Summarization of different breast imaging techniques: mammography, thermography, ultrasound, and MRI.
  • Analysis of data availability and DL model performance in diagnostic tasks.

Main Results:

  • Deep learning models demonstrate potential in improving the accuracy and efficiency of breast cancer detection across various imaging modalities.
  • The review identifies key DL architectures and their suitability for specific screening tasks.
  • Data availability and quality are critical factors influencing DL model generalizability.

Conclusions:

  • AI, particularly deep learning, offers significant opportunities to enhance breast cancer screening protocols.
  • Challenges remain in clinical implementation, including data privacy, regulatory approval, and model interpretability.
  • Further research is needed to optimize DL algorithms and facilitate seamless integration into routine clinical practice.