Advanced deep learning and large language models: Comprehensive insights for cancer detection

Abstract

In recent years, the rapid advancement of machine learning (ML), particularly deep learning (DL), has revolutionized various fields, with healthcare being one of the most notable beneficiaries. DL has demonstrated exceptional capabilities in addressing complex medical challenges, including the early detection and diagnosis of cancer. Its superior performance, surpassing both traditional ML methods and human accuracy, has made it a critical tool in identifying and diagnosing diseases such as cancer. Despite the availability of numerous reviews on DL applications in healthcare, a comprehensive and detailed understanding of DL’s role in cancer detection remains lacking. Most existing studies focus on specific aspects of DL, leaving significant gaps in the broader knowledge base. This paper aims to bridge these gaps by offering a thorough review of advanced DL techniques, namely transfer learning (TL), reinforcement learning (RL), federated learning (FL), Transformers, and large language models (LLMs). These cutting-edge approaches are pushing the boundaries of cancer detection by enhancing model accuracy, addressing data scarcity, and enabling decentralized learning across institutions while maintaining data privacy. TL enables the adaptation of pre-trained models to new cancer datasets, significantly improving performance with limited labeled data. RL is emerging as a promising method for optimizing diagnostic pathways and treatment strategies, while FL ensures collaborative model development without sharing sensitive patient data. Furthermore, Transformers and LLMs, traditionally utilized in natural language processing (NLP), are now being applied to medical data for enhanced interpretability and context-based predictions. In addition, this review explores the efficiency of the aforementioned techniques in cancer diagnosis, it addresses key challenges such as data imbalance, and proposes potential solutions. It aims to be a valuable resource for researchers and practitioners, offering insights into current trends and guiding future research in the application of advanced DL techniques for cancer detection.