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<p>This review provides a systematic evaluation of recent advance in breast cancer detection using deep learning, fuzzy logic, and hybrid computational models, with a focus on ultrasound and mammography imaging. Unlike existing surveys that examine these methods in isolation, this work addresses a key gap by analyzing 42 studies published between 2019 and 2025, that integrate deep learning with fuzzy clustering and optimization techniques. Finding show that such hybrid approaches improve lesion segmentation and classification accuracy while mitigating limitations such as image noise, operator dependency, and low interpretability. The BUSI datasets emerged as most widely used benchmark for validation. However, persistent challenges remain, including limited datasets size, lack of external validation, and inconsistent performance reporting, which restrict clinical translation. By Comparing methods, performance and limitation, this review highlights the potential of hybrid AI frameworks to provide more reliable diagnostic support for early breast cancer detection. It concludes that future progress requires larger, multi-institutional datasets, standardized evaluation protocols, and an interpretable hybrid model to enable practical deployment in real-world applications.</p>

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Performance Evaluation of Existing Hybrid Models for Breast Cancer Diagnosis: A Review of Deep Learning and Fuzzy Clustering Approaches in Medical Imaging

  • 期刊论文

  • 发表日期：

    2025年10月24日

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  • Geeta Agarwal,
  • Sonam Seth

摘要

This review provides a systematic evaluation of recent advance in breast cancer detection using deep learning, fuzzy logic, and hybrid computational models, with a focus on ultrasound and mammography imaging. Unlike existing surveys that examine these methods in isolation, this work addresses a key gap by analyzing 42 studies published between 2019 and 2025, that integrate deep learning with fuzzy clustering and optimization techniques. Finding show that such hybrid approaches improve lesion segmentation and classification accuracy while mitigating limitations such as image noise, operator dependency, and low interpretability. The BUSI datasets emerged as most widely used benchmark for validation. However, persistent challenges remain, including limited datasets size, lack of external validation, and inconsistent performance reporting, which restrict clinical translation. By Comparing methods, performance and limitation, this review highlights the potential of hybrid AI frameworks to provide more reliable diagnostic support for early breast cancer detection. It concludes that future progress requires larger, multi-institutional datasets, standardized evaluation protocols, and an interpretable hybrid model to enable practical deployment in real-world applications.