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Related Experiment Video

Updated: Oct 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis.

Muhammad Firoz Mridha1, Md Abdul Hamid2, Muhammad Mostafa Monowar2

  • 1Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.

Cancers
|December 10, 2021
PubMed
Summary

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This review explores deep learning for breast cancer detection, highlighting evolving architectures and imaging modalities for early diagnosis. It analyzes current methods, datasets, and future research directions to improve breast cancer outcomes.

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Breast cancer is the most frequently diagnosed cancer in women, with increasing incidence.
  • Early detection and treatment significantly improve recovery chances.
  • Deep learning methods show promise for accurate breast cancer cell prediction using medical imaging.

Purpose of the Study:

  • To review emerging deep learning architectures for breast cancer detection.
  • To analyze existing studies, datasets, and image pre-processing techniques.
  • To provide a comprehensive overview of imaging modalities, performance, challenges, and future research directions.

Main Methods:

  • Systematic review of deep learning-based breast cancer diagnosis studies.
  • Analysis of various deep learning architectures and their performance.
Keywords:
breast cancer diagnosisimage pre-processingimaging modalitiesneural networks

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  • Examination of diverse medical imaging modalities and datasets used in breast cancer research.

    • Main Results:

    • Identified evolving deep learning architectures and their strengths/limitations.
    • Reviewed image pre-processing techniques and their impact on diagnostic accuracy.
    • Summarized performance metrics and results across different studies and modalities.

    Conclusions:

    • Deep learning offers powerful tools for enhancing breast cancer detection accuracy.
    • Further research is needed to address limitations and explore novel architectures and modalities.
    • This review provides a roadmap for future advancements in AI-driven breast cancer diagnosis.