Breast cancer detection using artificial intelligence techniques: A systematic literature review

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Breast cancer is a leading human disease with significant incidence rates.
• Early diagnosis dramatically improves survival rates, with 64% of cases diagnosed early achieving 99% survival.
• Histopathological imaging is a common, cost-effective method for breast cancer detection compared to genetic analysis.

Purpose of the Study:

• To systematically review existing research on breast cancer detection and treatment.
• To evaluate the application of deep learning (DL) and machine learning (ML) in this field.
• To provide recommendations for future research directions.

Main Methods:

• Systematic literature review of studies utilizing genetic sequencing or histopathological imaging.
• Analysis of DL and ML techniques applied to breast cancer data.
• Synthesis of findings to identify trends and gaps in current research.

Main Results:

• AI and ML show promise in enhancing the accuracy and efficiency of breast cancer detection and treatment.
• Histopathological imaging combined with DL/ML is a viable approach for early diagnosis.
• The review consolidates current knowledge and identifies areas for further investigation.

Conclusions:

• AI and ML are valuable tools for advancing breast cancer diagnostics and therapeutics.
• Further research is needed to optimize DL/ML models for clinical application.
• This review offers a roadmap for researchers in the field of AI-driven breast cancer analysis.