TY  - JOUR
N2  - 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.
A1  - Saleem, Alveena
A1  - Umair, Muhammad
A1  - Naseem, Muhammad Tahir
A1  - Zubair, Muhammad
A1  - Aparicio Obregón, Silvia
A1  - Calderón Iglesias, Rubén
A1  - Hassan, Shoaib
A1  - Ashraf, Imran
EP  - 4337
JF  - Journal of Cancer
SP  - 4316
UR  - http://repositorio.unic.co.ao/id/eprint/17863/
AV  - public
IS  - 15
PB  - Ivyspring International Publisher
VL  - 16
ID  - unic17863
KW  - tumor detection
KW  -  breast cancer
KW  -  deep learning
KW  -  segmentation
TI  - Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection
Y1  - 2025/10//
ER  -