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Recent Advances in Breast Cancer Detection: A Review on Segmentation and Classification Techniques
Review Article | Published: 07 November 2025
ICCK Journal of Image Analysis and Processing Volume 1, Issue 4, 2025: 147-161
Volume 1, Issue 4, ICCK Journal of Image Analysis and Processing
Volume 1, Issue 4, 2025
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ICCK Journal of Image Analysis and Processing, Volume 1, Issue 4, 2025: 147-161

Open Access | Review Article | 07 November 2025
Recent Advances in Breast Cancer Detection: A Review on Segmentation and Classification Techniques
by
Muhammad Moosa Raza Khan Muhammad Moosa Raza Khan 1
Mehwish Zafar Mehwish Zafar 1 *
Abdul Majid Abdul Majid 1
Fadia Ali Khan Fadia Ali Khan 1
1 Department of Computer Science, HITEC University Taxila, 47080 Taxila, Pakistan
* Corresponding Author: Mehwish Zafar, [email protected]
DOI: 10.62762/JIAP.2025.780624
Received: 31 August 2025, Accepted: 18 September 2025, Published: 07 November 2025  
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Abstract
Breast Cancer (BC) is still one of the most significant, life-threatening, and prevalent diseases that affects women all around the globe. The early recognition and strategies of effective treatment measures improve the rate of survival among patients significantly, contributing to a critical research area in medical science. This review presents a comprehensive review of recent trends and advancements in the recognition of BC recognition, diagnosis, and treatment. It covers multiple imaging modalities, including Magnetic Resonance Imaging (MRI), ultrasound, mammography, and histopathology, along with various approaches of Machine Learning (ML) and Deep Learning (DL) that enhance the efficiency of diagnosis and improve accuracy. In particular, in some recent decades, the Computer-Aided Diagnosis (CAD) system gained noteworthy attention for the recognition of affected regions, for the minimization of diagnostic rates, and for assisting radiologists. ML algorithms have robust capabilities for feature extraction and classification, while the DL models, specifically Convolutional Neural Networks (CNNs), have shown remarkable performance in image-based diagnostic tasks. Moreover, the presented review also discusses some challenges, such as dense breast tissue, noise, and tumor heterogeneity, along with the limitations of the current studies. The review concludes with a compact summary of current trends, research problems, and future directions for the improvement of BC detection.
Graphical Abstract
Recent Advances in Breast Cancer Detection: A Review on Segmentation and Classification Techniques
Keywords
breast cancer
tumor detection
early diagnosis
computer-aided diagnosis
machine learning
deep learning
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Khan, M. M. R., Zafar, M., Majid, A., & Khan, F. A. (2025). Recent Advances in Breast Cancer Detection: A Review on Segmentation and Classification Techniques. ICCK Journal of Image Analysis and Processing, 1(4), 147–161. https://doi.org/10.62762/JIAP.2025.780624
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