Artificial intelligence for breast cancer analysis: Trends & directions

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Oncology

Background:

• Breast cancer is a leading cause of mortality in women globally.
• Early detection significantly improves patient outcomes and survival rates.
• Automated detection systems are crucial for timely diagnosis.

Purpose of the Study:

• To review AI and computer vision methods for automated breast cancer detection.
• To analyze various imaging modalities, including mammograms, ultrasound, and MRI.
• To identify strengths, limitations, and dataset resources for AI research in breast imaging.

Main Methods:

• Comprehensive literature review of AI and computer vision techniques.
• Analysis of different medical imaging modalities for breast cancer detection.
• Focus on AI frameworks utilizing mammograms due to widespread use and data availability.

Main Results:

• AI algorithms, particularly deep learning, show promise in automating breast cancer detection.
• Mammography is a primary imaging modality, with available datasets facilitating AI development.
• Various imaging techniques offer complementary information for diagnosis.

Conclusions:

• AI frameworks are advancing automated breast cancer detection, especially using mammograms.
• Availability of robust datasets is critical for training effective AI algorithms.
• This review serves as a resource for researchers in automated breast imaging analysis.