Advances in Cancer Detection using Machine Learning: A Comprehensive Review and Future Prospects

Abstract

Cancer remains a leading cause of mortality worldwide, making early detection crucial for improving patient outcomes and survival rates. Machine learning (ML) techniques have shown promising potential in revolutionizing cancer detection through their ability to analyze complex and high-dimensional data. This research paper presents a comprehensive review of the recent advances in cancer detection using machine learning, covering a wide range of cancer types and imaging modalities. We explore the application of various ML algorithms, including deep learning, support vector machines, and random forests, in analyzing medical imaging, genomics, and biomarker data to identify cancerous patterns with high accuracy and efficiency. Additionally, we discuss the challenges and limitations associated with ML-based cancer detection, such as the need for large and diverse datasets, interpretability of model outputs, and generalizability across different populations. Furthermore, we highlight the potential future prospects of integrating machine learning with emerging technologies like liquid biopsies, wearable devices, and multi-omics data to enhance cancer detection sensitivity and specificity. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in cancer detection using machine learning, fostering collaboration and innovation towards more effective and personalized cancer screening and diagnosis.