Abstract
Deep learning (DL) has revolutionized cancer detection accuracy, speed, and accessibility. Leveraging sophisticated algorithms, DL has demonstrated transformative potential across diverse applications, including imaging-based diagnostics and genomic analysis, ultimately leading to better detection, improved patient treatment outcomes, and decreased overall mortality rates. Despite its promise, integrating DL into clinical practice presents substantial challenges, including limitations in data quality and standardization, as well as ethical and regulatory concerns, and the need for model interpretability and transparency. This review provides a comprehensive analysis of recent research (2018-2024) retrieved from PubMed and IEEE Xplore databases, encompassing 1304 studies from PubMed and 115 from IEEE, to highlight the current applications, opportunities, and challenges of DL in oncology. Additionally, this paper explores emerging solutions, including federated learning, explainable artificial intelligence, and synthetic data generation, to address these barriers. The review also emphasizes the importance of interdisciplinary collaboration, the integration of next-generation artificial intelligence techniques, and the adoption of multimodal data approaches to improve diagnostic precision and support personalized cancer treatment. By systematically analyzing key developments and challenges, this review aims to guide future research and DL technologies in oncology, promoting equitable and impactful advancements in cancer care.