Abstract: Breast cancer is a prevalent and life-threatening disease affecting millions of women globally. Early and accurate diagnosis is crucial for successful treatment and improved patient outcomes. In recent years, machine learning has emerged as a powerful tool in the field of medical imaging and diagnostics, offering potential advancements in breast cancer detection and classification.

Several machine learning algorithms, including support vector machines (SVM), artificial neural networks (ANN), random forests, and convolutional neural networks (CNN), are employed to build predictive models based on the extracted features. The models are trained and evaluated using a comprehensive dataset comprising a diverse range of breast images, annotated by experienced radiologists.

Keywords: Breast Cancer, convolutional neural networks (CNNs)

| DOI: 10.17148/IARJSET.2024.11794