Overview

The most common cancer among the women in the world with high mortality rate is breast cancer. Early detection and accurate classification of breast cancer are essential for improving patient survival and enabling timely clinical intervention. Machine learning techniques have been widely applied to analyze diagnostic data from mammography, ultrasound, magnetic resonance imaging, and histopathological examinations. Traditional machine learning methods use handcrafted features classified by algorithms such as Support Vector Machines and Random Forests, while deep learning models, particularly convolutional neural networks, automatically learn discriminative feature representations from raw data. Experimental studies show that these models can effectively distinguish between benign and malignant lesions with high accuracy, supporting radiologists in early-stage diagnosis.

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