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Breast cancer ranks second among global cancers in women, with around 2.3 million diagnosed cases and 685,000 fatalities in 2020. It affects men too but is more prevalent in women. Breast cancer's development is linked to cellular abnormalities, and it can be categorized as in situ or invasive ductal carcinoma, requiring different treatments. Medical technology advancements have contributed to diagnosing breast cancer and reducing mortality rates. Deep Learning techniques in medical imaging are gaining prominence. This review explores the impact of various Machine Learning Technologies (MLTs) for breast cancer detection, highlighting the advantages of Deep Learning in the medical field. It offers valuable insights and encourages further research in this domain.

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Effect of Various Machine Learning Techniques Considering Different Modalities for Breast Lesions Malignancy Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Priyanka Puvar,
  • Bhagirath Prajapati

摘要

Breast cancer ranks second among global cancers in women, with around 2.3 million diagnosed cases and 685,000 fatalities in 2020. It affects men too but is more prevalent in women. Breast cancer's development is linked to cellular abnormalities, and it can be categorized as in situ or invasive ductal carcinoma, requiring different treatments. Medical technology advancements have contributed to diagnosing breast cancer and reducing mortality rates. Deep Learning techniques in medical imaging are gaining prominence. This review explores the impact of various Machine Learning Technologies (MLTs) for breast cancer detection, highlighting the advantages of Deep Learning in the medical field. It offers valuable insights and encourages further research in this domain.