August 20, 2025BioMedInformaticsOpen Access

Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches

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Authors

MRMd. Atiqur RahmanIslamic University of Technology MKMusharrat KhanEast West University YWYutaka WatanobeUniversity of Aizu

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Implication

This review highlights improvements in breast cancer detection accuracy using imaging modalities and machine learning approaches, indicating significant potential for integrated diagnostic systems.

Key Points

Deep learning models achieve 90–99% accuracy in breast cancer detection across various imaging modalities, enhancing diagnostic capabilities.
Convolutional neural networks show particularly high accuracy rates in mammography (99.96%) and ultrasound (100%) applications, indicating advanced detection technology.
Hybrid models that integrate imaging and clinical data significantly improve early detection rates, emphasizing the benefits of a comprehensive approach.
Lifestyle modifications, including dietary changes and BMI management, can substantially reduce breast cancer risk, highlighting preventative strategies.

Cite This Study

Rahman et al. (2025) studied this question.

synapsesocial.com/papers/68af75147567bf4f94fee183 https://doi.org/10.3390/biomedinformatics5030046

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Implication

This review highlights improvements in breast cancer detection accuracy using imaging modalities and machine learning approaches, indicating significant potential for integrated diagnostic systems.

Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

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