Vol. 4, Issue 8, Part D (2018)

The relentless pursuit of advancements in medical diagnostics has led to a paradigm shift in the field of cancer detection, with deep learning emerging as a powerful tool for early identification. This comprehensive review delves into the evolutionary trajectory of deep learning architectures employed in the realm of cancer detection, highlighting the transformative impact on early diagnosis.

The review commences by elucidating the urgency and significance of early cancer detection, underscoring the potential to significantly enhance patient outcomes and reduce mortality rates. Subsequently, it navigates through the chronological progression of deep learning architectures, providing a nuanced exploration of their evolution and adaptation to the intricacies of cancer detection.

The initial phase of the review addresses the foundational convolutional neural networks (CNNs) that laid the groundwork for deep learning in medical imaging. With seminal models like Alex Net, VGG, and ResNet, the efficacy of CNNs in extracting intricate patterns from medical images is scrutinized, revealing their pioneering role in shaping subsequent developments.

The narrative then shifts towards the integration of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, extending the capabilities of deep learning to temporal data analysis. This evolution is particularly significant in the context of dynamic imaging modalities, such as functional magnetic resonance imaging (fMRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where temporal patterns play a crucial role in early cancer detection.

As the review progresses, attention is directed towards the symbiotic relationship between deep learning and radiomics, exploring how the fusion of radiomic features and deep learning architectures enhances the discrimination power of cancer detection models. The emergence of attention mechanisms, transfer learning, and ensemble methods further amplifies the diagnostic accuracy, fostering a holistic approach to early cancer detection.