Saleem, Alveena and Umair, Muhammad and Naseem, Muhammad Tahir and Zubair, Muhammad and Aparicio Obregón, Silvia and Calderón Iglesias, Rubén and Hassan, Shoaib and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2025) Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection. Journal of Cancer, 16 (15). pp. 4316-4337.
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Abstract
Breast cancer is a lethal carcinoma impacting a considerable number of women across the globe. While preventive measures are limited, early detection remains the most effective strategy. Accurate classification of breast tumors into benign and malignant categories is important which may help physicians in diagnosing the disease faster. This survey investigates the emerging inclination and approaches in the area of machine learning (ML) for the diagnosis of breast cancer, pointing out the classification techniques based on both segmentation and feature selection. Certain datasets such as the Wisconsin Diagnostic Breast Cancer Dataset (WDBC), Wisconsin Breast Cancer Dataset Original (WBCD), Wisconsin Prognostic Breast Cancer Dataset (WPBC), BreakHis, and others are being evaluated in this study for the demonstration of their influence on the performance of the diagnostic tools and the accuracy of the models such as Support vector machine, Convolutional Neural Networks (CNNs) and ensemble approaches. The main shortcomings or research gaps such as prejudice of datasets, scarcity of generalizability, and interpretation challenges are highlighted. This research emphasizes the importance of the hybrid methodologies, cross-dataset validation, and the engineering of explainable AI to narrow these gaps and enhance the overall clinical acceptance of ML-based detection tools.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | tumor detection, breast cancer, deep learning, segmentation |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 27 Oct 2025 09:40 |
| Last Modified: | 10 Nov 2025 08:21 |
| URI: | http://repositorio.funiber.org/id/eprint/17863 |
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