Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection

Article Subjects > Engineering <http://repositorio.unib.org/view/subjects/uneat=5Feng.html> Europe University of Atlantic > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html>
Ibero-american International University > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uninimx=5Fproduccion=5Fcientifica.html>
Ibero-american International University > Research > Articles and Books <http://repositorio.unib.org/view/divisions/uninipr=5Fproduccion=5Fcientifica.html>
Universidad Internacional do Cuanza > Research > Scientific Production <http://repositorio.unib.org/view/divisions/unic=5Fproduccion=5Fcientifica.html>
University of La Romana > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uniromana=5Fproduccion=5Fcientifica.html> Open English 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. metadata Saleem, Alveena; Umair, Muhammad; Naseem, Muhammad Tahir; Zubair, Muhammad; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Hassan, Shoaib and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED, UNSPECIFIED <http://repositorio.unib.org/id/eprint/17863/1/v16p4316.pdf> (2025) Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection. Journal of Cancer, 16 (15). pp. 4316-4337.