Computer-aided breast cancer detection using mammograms: a review.

Area of Science:

• Medical Imaging
• Radiology
• Computational Mathematics

Background:

• Mammography is a key breast cancer screening tool, but interpretation variability affects accuracy.
• Inter-observer and intra-observer errors in mammogram analysis are significant challenges.
• Current screening lacks a definitive gold standard due to variability in image quality and radiologist expertise.

Purpose of the Study:

• To provide an overview of recent advances in computer-aided diagnosis (CAD) for breast cancer detection using mammograms.
• To specifically focus on the mathematical techniques employed in breast cancer image analysis.
• To serve as a mathematical primer for researchers and practitioners in the field.

Main Methods:

• Review of existing literature on computer-aided diagnosis (CAD) systems for mammography.
• Identification and categorization of mathematical approaches used in breast cancer image analysis.
• Synthesis of information on the application of mathematical methods in CAD systems.

Main Results:

• Variability in mammogram interpretation necessitates automated diagnostic tools.
• Mathematical techniques are crucial for standardizing breast cancer diagnosis and grading.
• This review highlights the importance of mathematical foundations in developing robust CAD systems.

Conclusions:

• Automated techniques, particularly those leveraging mathematical methods, are essential for improving breast cancer detection accuracy.
• A deeper understanding of mathematical aspects in CAD can lead to more reliable breast cancer screening.
• Standardizing diagnostic procedures through mathematical approaches can enhance early detection and patient outcomes.