eprintid: 17863 rev_number: 10 eprint_status: archive userid: 2 dir: disk0/00/01/78/63 datestamp: 2025-10-27 09:40:28 lastmod: 2025-10-30 19:04:48 status_changed: 2025-10-27 09:40:28 type: article metadata_visibility: show creators_name: Saleem, Alveena creators_name: Umair, Muhammad creators_name: Naseem, Muhammad Tahir creators_name: Zubair, Muhammad creators_name: Aparicio Obregón, Silvia creators_name: Calderón Iglesias, Rubén creators_name: Hassan, Shoaib creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: silvia.aparicio@uneatlantico.es creators_id: ruben.calderon@uneatlantico.es creators_id: creators_id: title: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: tumor detection, breast cancer, deep learning, segmentation 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. date: 2025-10 publication: Journal of Cancer volume: 16 number: 15 publisher: Ivyspring International Publisher pagerange: 4316-4337 refereed: TRUE access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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 y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection. Journal of Cancer, 16 (15). pp. 4316-4337. document_url: http://repositorio.unib.org/id/eprint/17863/1/v16p4316.pdf