Abstract: Breast cancer is one of the most prevalent and life-threatening diseases affecting women globally, where early detection plays a crucial role in reducing mortality rates. In recent years, emerging Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) and Deep Learning (DL), have shown remarkable potential in improving the accuracy and efficiency of breast cancer detection and diagnosis. This systematic review presents a comprehensive overview of global research efforts that leverage AI-based methodologies across various medical imaging modalities, including mammography, ultrasound, magnetic resonance imaging (MRI), and histopathological imaging.
The study reviews traditional ML algorithms such as Support Vector Machines (SVM), Decision Trees, Random Forests, and k-Nearest Neighbors (k-NN), alongside advanced DL architectures including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and transfer learning models. The analysis highlights that DL approaches, especially CNNs, significantly outperform conventional ML techniques due to their ability to automatically extract complex features from large-scale datasets. Additionally, the review discusses hybrid and ensemble models that combine ML and DL techniques to enhance predictive performance.
Key challenges identified include limited availability of high-quality annotated datasets, class imbalance, overfitting, lack of interpretability, and issues related to generalization across diverse populations and imaging systems. The review also emphasizes the growing importance of explainable AI (XAI), data privacy, and ethical considerations in clinical deployment. Comparative insights from global studies reveal varying levels of accuracy and robustness depending on data sources, preprocessing techniques, and evaluation metrics.
Overall, the findings suggest that AI-driven approaches hold significant promise in supporting radiologists, improving diagnostic accuracy, and enabling early-stage detection of breast cancer. Future research directions focus on developing standardized datasets, improving model transparency, and fostering interdisciplinary collaboration to ensure reliable and scalable real-world applications.

Keywords: Breast Cancer Detection, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNN), Medical Imaging, Mammography, Ultrasound Imaging, MRI.

Downloads: | DOI: 10.17148/IARJSET.2026.13474