Systematic Review on Breast Cancer Prediction and Classification by using Machine Learning and Deep Learning Methods
Sampoornamma Sudarsa, R. Pradeep Kumar Reddy · 2024
Breast cancer remains a leading cause of death among women globally, highlighting the importance of accurate prediction and classification for effective diagnosis and treatment. This comprehensive literature review investigates the application of machine learning (ML) and deep learning (DL) methods in breast cancer research. By examining recent research, the review categorizes studies based on methodological approaches, datasets, and performance indicators. Key ML approaches, including support vector machines, decision trees, and ensemble methods, are explored alongside DL architectures like convolutional neural networks and recurrent neural networks. The review discusses advancements in feature extraction, model optimization, and interpretability, while also addressing challenges such as data heterogeneity and ethical considerations. The findings highlight the promising potential of ML and DL in improving breast cancer outcomes, while acknowledging the need for further research to address existing limitations and facilitate clinical integration. This review provides valuable insights for researchers and practitioners seeking to apply ML and DL techniques for breast cancer prediction and classification, contributing to the advancement of this critical field.