A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition
Breast cancer remains one of the leading causes of death among women worldwide, highlighting the need for early and accurate detection. Recent advancements in AI-driven techniques, particularly Machine Learning (ML) learning and Deep Learning (DL), have significantly improved breast cancer diagnosti...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000854 |
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| author | Kashif Khan Suryanti Awang Mohammed Ahmed Talab Hasan Kahtan |
| author_facet | Kashif Khan Suryanti Awang Mohammed Ahmed Talab Hasan Kahtan |
| author_sort | Kashif Khan |
| collection | DOAJ |
| description | Breast cancer remains one of the leading causes of death among women worldwide, highlighting the need for early and accurate detection. Recent advancements in AI-driven techniques, particularly Machine Learning (ML) learning and Deep Learning (DL), have significantly improved breast cancer diagnostics in breast cancer recognition. However, the intraclass variability, which is the subtle difference between malignant and benign within the same class, is a major challenge that leads to misclassification, misdiagnosis, reduced model effectiveness, increased health costs, and challenges clinical decision-making. This review provides a comprehensive analysis of ML and DL-based techniques, with a particular focus on addressing intra-class variance in breast cancer imaging. We reviewed articles from the past five years, examining publicly available datasets, the limitations and advantages of various ML and DL techniques, performance metrics, and the clinical applicability of multiple approaches. Furthermore, we also identified dataset challenges, including solutions to imbalanced datasets, particularly focusing on GAN-based data augmentation. We synthesized the current trends and highlighted the future directions. This review aims to support researchers and professionals in developing more robust and interpretable AI-driven breast diagnostic systems. |
| format | Article |
| id | doaj-art-dd4e4f1c83664dbeaa260cd4a3151a9a |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-dd4e4f1c83664dbeaa260cd4a3151a9a2025-08-20T03:24:44ZengElsevierFranklin Open2773-18632025-06-011110029610.1016/j.fraope.2025.100296A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognitionKashif Khan0Suryanti Awang1Mohammed Ahmed Talab2Hasan Kahtan3Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang 26600, MalaysiaFaculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang 26600, Malaysia; Corresponding author.Department of Medical Physics, College of Applied Sciences, Al-Fallujah University, IraqCreative Computing Research Centre (CCRC), Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Western Ave, Cardiff CF5 2YB, United KingdomBreast cancer remains one of the leading causes of death among women worldwide, highlighting the need for early and accurate detection. Recent advancements in AI-driven techniques, particularly Machine Learning (ML) learning and Deep Learning (DL), have significantly improved breast cancer diagnostics in breast cancer recognition. However, the intraclass variability, which is the subtle difference between malignant and benign within the same class, is a major challenge that leads to misclassification, misdiagnosis, reduced model effectiveness, increased health costs, and challenges clinical decision-making. This review provides a comprehensive analysis of ML and DL-based techniques, with a particular focus on addressing intra-class variance in breast cancer imaging. We reviewed articles from the past five years, examining publicly available datasets, the limitations and advantages of various ML and DL techniques, performance metrics, and the clinical applicability of multiple approaches. Furthermore, we also identified dataset challenges, including solutions to imbalanced datasets, particularly focusing on GAN-based data augmentation. We synthesized the current trends and highlighted the future directions. This review aims to support researchers and professionals in developing more robust and interpretable AI-driven breast diagnostic systems.http://www.sciencedirect.com/science/article/pii/S2773186325000854Intraclass varianceBreast cancer classificationDeep learningMachine learning |
| spellingShingle | Kashif Khan Suryanti Awang Mohammed Ahmed Talab Hasan Kahtan A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition Franklin Open Intraclass variance Breast cancer classification Deep learning Machine learning |
| title | A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition |
| title_full | A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition |
| title_fullStr | A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition |
| title_full_unstemmed | A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition |
| title_short | A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition |
| title_sort | comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition |
| topic | Intraclass variance Breast cancer classification Deep learning Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000854 |
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