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

Area of Science:

• Oncology
• Biomedical Engineering
• Data Science

Background:

• Cancer remains a significant global health concern, with early detection being paramount for survival.
• Integrating genomic and imaging data is increasingly vital for accurate cancer diagnosis.
• Deep learning techniques offer powerful tools for analyzing complex biological data.

Purpose of the Study:

• To review the advancements in deep learning applications for cancer detection using combined genomic and imaging data.
• To examine current deep learning models, their specific applications, inherent challenges, and future research trajectories.
• To highlight the potential of deep learning in revolutionizing early cancer screening and personalized medicine.

Main Methods:

• Review of current literature on deep learning models (e.g., CNNs) applied to cancer detection.
• Analysis of studies integrating genomic and imaging data for diagnostic purposes.
• Examination of the methodologies employed in feature extraction and model training.

Main Results:

• Deep learning models, especially CNNs, demonstrate significant potential in autonomously extracting features from large datasets.
• The integration of genomic and imaging data with deep learning enhances early cancer detection accuracy.
• Innovative deep learning approaches are paving the way for precision diagnosis.

Conclusions:

• Deep learning, utilizing genomic and imaging data, offers a promising avenue for improving early cancer detection rates.
• Continued research and development in this area are crucial for advancing precision oncology and personalized treatment.
• Deep learning facilitates significant progress in early cancer screening technologies and patient outcomes.