Machine Learning Approaches in Breast Cancer Diagnosis: Current Trends and Future Perspectives - Payel Chakraborty; Shubhankar Jha (International Journal of Radio Frequency Innovations, 2024)
Since cancer is still one of the world's top causes of death, precise and effective detection techniques must be developed. Machine learning (ML) approaches have shown promise in recent years for enhancing cancer prognosis and detection. This paper presents a comprehensive review of the application of ML in cancer detection, focusing on various modalities including medical imaging, genomic data, and clinical records. We highlight the challenges associated with traditional cancer detection methods and discuss how ML algorithms can address these limitations by analyzing complex patterns and extracting meaningful features from diverse datasets. Additionally, we look at the most recent developments in machine learning (ML)-based cancer detection systems, such as deep learning models, the use of support vector machines (SVMs), and convolutional neural networks (CNNs). We also discuss the importance of large-scale datasets and feature selection techniques in training accurate ML models for cancer detection. Additionally, we review the integration of multimodal data fusion techniques to improve the performance of ML-based cancer detection systems. Additionally, we go over the difficulties and ethical issues surrounding the application of machine learning (ML) techniques in healthcare contexts, including bias mitigation, interpretability, and reproducibility. Finally, we outline future research directions and potential opportunities for the development of more robust and reliable ML-based cancer detection systems. This review aims to provide insights into the current state-of-the-art, challenges, and prospects of utilizing ML for enhancing cancer detection, ultimately contributing to improved patient outcomes and personalized treatment strategies
Keywords: Heterogeneous; benign; convolutional; malignant