Maximizing Diagnostic Efficiency: Ensemble Deep Learning Approaches in Cancer Detection

Effective cancer diagnosis plays a critical role in determining patient outcomes; thus, it has created its leading place in modern healthcare practice. Deep learning is the part of artificial intelligence that attains its significant impact in conducting cancer detection work for all kinds of data modality, from medical imaging to genomics. This review article takes into consideration the complex relationship between deep learning, including such well-known models as CNNs and transformers, and ensemble methods for cancer diagnosis. The review focuses particularly on combining different deep learning models with their collaborative strength toward enhancing diagnostic accuracy. Although the ensemble of deep learning has been marked by great prospective contribution, the review tries to outline existing limitations of the approach and future directions for research, paying a central role to this approach. By underscoring its paramount importance, this review seeks to enlighten researchers and practitioners about the potential of ensemble techniques in amplifying diagnostic accuracy, thereby contributing to enhancing global healthcare systems. The review endeavors to advance knowledge within this crucial domain by discerning current research and prospects.