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Open Access

REVIEW

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

Hanifah Rahmi Fajrin1,2, Se Dong Min1,3,*
1 Department of Software Convergence, Soonchunhyang University, Asan, 31538, Republic of Korea
2 Department of Medical Electronics Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta, 55183, Indonesia
3 Department of Medical IT Engineering, Soonchunhyang University, Asan, 31538, Republic of Korea
* Corresponding Author: Se Dong Min. Email: email
(This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072609

Received 30 August 2025; Accepted 28 November 2025; Published online 19 December 2025

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. 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.

Keywords

Breast cancer; mammogram segmentation; deep learning; machine learning; hybrid model

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