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Recent developments in DL and ML for breast cancer detection have greatly increased diagnostic accuracy; some models now exceed 98%. Research indicates that using cutting-edge feature extraction methods and sophisticated decision-support systems on mammograms and histopathological images has improved accuracy while lowering false positives (Obayya et al. in Cancers 15:885, 2023; Wang et al. in Electronics 11:2767, 2022). Cancer detection and prognosis are being revolutionized by ML and DL, despite obstacles such as high computational demands and dataset variability. Building more efficient and trustworthy AI-driven diagnostic systems requires addressing issues with multi-modal data fusion, model interpretability, and reliable validation in order to optimize their impact on patient care and healthcare services.

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Developments in Breast Cancer Detection: An Exensive Analysis of Deep Learning and Machine Learning Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Rachna Narula,
  • Vijay Kumar,
  • Kunal Sharma,
  • Monica Gupta,
  • Anil Kumar

摘要

Recent developments in DL and ML for breast cancer detection have greatly increased diagnostic accuracy; some models now exceed 98%. Research indicates that using cutting-edge feature extraction methods and sophisticated decision-support systems on mammograms and histopathological images has improved accuracy while lowering false positives (Obayya et al. in Cancers 15:885, 2023; Wang et al. in Electronics 11:2767, 2022). Cancer detection and prognosis are being revolutionized by ML and DL, despite obstacles such as high computational demands and dataset variability. Building more efficient and trustworthy AI-driven diagnostic systems requires addressing issues with multi-modal data fusion, model interpretability, and reliable validation in order to optimize their impact on patient care and healthcare services.