Abstract
Lung cancer continues to pose a significant global health challenge. To overcome this challenge, continuous advancements are being achieved in diagnostic methodologies to enhance early detection and improve patient outcomes. This chapter provides a thorough examination of recent progress in lung cancer diagnosis through an extensive survey of deep learning approaches. Focusing on the integration of artificial intelligence (AI) techniques with medical imaging, the chapter encompasses an analysis of convolutional neural networks (CNNs), recurrent neural networks (RNNs), including long short-term memory (LSTMs) networks, and generative-pretrained transformers (GPTs) or large language models (LLMs). The chapter delves into the evolution of deep learning models for lung cancer detection, emphasizing their performance in image classification, lesion segmentation, and overall diagnostic accuracy. Additionally, we also showcase the literature that explores the integration of diverse imaging modalities, such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), within deep learning frameworks to enhance the robustness and reliability of diagnostic systems. Furthermore, the review addresses the challenges inherent in the exploration of deep learning in lung cancer diagnosis, including issues related to data quality, model interpretability, and generalizability. Strategies to address these challenges, such as transfer learning, data augmentation (based on generative adversarial networks), and transformers, are thoroughly discussed. The comprehensive analysis presented in this chapter aims to provide a consolidated understanding of the current landscape of deep learning approaches in lung cancer diagnosis. By highlighting recent advancements, challenges, and potential solutions, this chapter contributes to the ongoing dialogue within the scientific community, fostering the development of more effective and reliable tools for early detection and management of lung cancer.
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Bouchaffra, D., Ykhlef, F., Benbelkacem, S. (2024). Advancement in Lung Cancer Diagnosis: A Comprehensive Review of Deep Learning Approaches. In: Interdisciplinary Cancer Research. Springer, Cham. https://doi.org/10.1007/16833_2024_302
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DOI: https://doi.org/10.1007/16833_2024_302
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