Deep Learning for Cancer Detection Based on Genomic and Imaging Data: A Comprehensive Review.

Abstract

Cancer is a major global health challenge, and early detection is critical to improving survival rates. Advances in genomics and imaging technologies have made the integration of genomic and imaging data a common practice in cancer detection. Deep learning, especially Convolutional Neural Networks (CNNs), demonstrates substantial potential for early cancer diagnosis by autonomously extracting valuable features from large-scale datasets, thus enhancing early detection accuracy. This review summarizes the progress in deep learning applications for cancer detection using genomic and imaging data. It examines current models, their applications, challenges, and future research directions. Deep learning introduces innovative approaches for precision diagnosis and personalized treatment, facilitating advancements in early cancer screening technologies.