Advancing Early Cancer Detection with Machine Learning
Upasana Sinha, J. Durga Prasad Rao, Suman Kumar Swarnkar, Prashant Kumar Tamrakar · 2023
Early detection of cancer plays a crucial role in improving patient outcomes and reducing mortality rates. Machine learning has emerged as a promising tool for early cancer detection, with the potential to analyze vast amounts of data and identify patterns that may not be immediately apparent to human experts. In this paper, we provide a comprehensive review of methods and applications for advancing early cancer detection with machine learning. We first present an overview of cancer detection using machine learning, including data preprocessing, feature extraction, feature selection, classification, and evaluation. We then discuss various types of data used for cancer detection, such as imaging, genomics, proteomics, and electronic health records. We also review different types of cancer, including breast, lung, prostate, and skin cancer, and highlight the specific challenges and opportunities for early detection with machine learning in each case. Finally, we discuss the current state-of-the-art in machine learning for early cancer detection, and present future directions and challenges for research in this area.