An Overview of Segmentation Techniques in Breast Cancer Detection: From Classical to Hybrid Model

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. By synthesizing current findings, this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection. Overall, our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources, while DL models are preferable when pixel-level annotations and resources are available, and hybrid pipelines are most appropriate when fine-grained clinical precision is required.