Hanifah Rahmi Fajrin1,2, Se Dong Min1,3,*
CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072609
- 12 January 2026
Abstract Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning. As research in this domain continues to expand, various segmentation techniques have been proposed across classical image processing, machine learning (ML), deep learning (DL), and hybrid/ensemble models. This study conducts a systematic literature review using the PRISMA methodology, analyzing 57 selected articles to explore how these methods have evolved and been applied. The review highlights the strengths and limitations of each approach, identifies commonly used public datasets, and observes emerging trends in model integration and clinical relevance. More >
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