Multimodal Deep Learning Approaches for Early Cancer Detection: A Systematic Literature Review

Identifying cancer at an early stage markedly improves patient prognosis, but traditional diagnostic approaches frequently fail to achieve the necessary sensitivity and specificity for prompt treatment. Multimodal deep learning has emerged as a promising approach to integrate diverse data sources, such as medical imaging, genomics, and clinical records, thereby improving diagnostic accuracy. This systematic literature review investigates the present status of multimodal deep learning approaches for early cancer detection, with particular attention to their methods, applications, and constraints. We determine essential patterns and obstacles by examining research that merges diverse data types, such as imaging and multi-omics, to improve predictive accuracy. The review highlights how these approaches address heterogeneity in data types and improve generalization across different cancer types. The study shows multimodal deep learning performs better than unimodal approaches, yet issues including merging data, understanding models, and high processing demands continue to be unaddressed. Additionally, we explore the prospects of these methods for screening non-cancerous conditions, highlighting their wider relevance in medical diagnostics. The results indicate future studies ought to focus on strong merging approaches, expandable frameworks, and medical verification to close the divide between experimental systems and practical application. This review delivers a thorough consolidation of prior research and presents perspectives on upcoming trends in multimodal deep learning for early cancer diagnosis.