Deep learning algorithms for lung cancer detection in CT images: A narrative review
DOI:
https://doi.org/10.12923/2083-4829/2026-0004Słowa kluczowe:
lung cancer, artificial intelligence, computed tomography (CT), deep learningAbstrakt
Lung cancer remains a leading cause of cancer-related mortality, primarily due to late-stage diagnosis. Deep learning (DL) algorithms, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for the early detection of pulmonary nodules in computed tomography (CT) images, demonstrating high sensitivity and specificity. This review explores the effectiveness and accuracy of these algorithms, highlighting their ability to surpass traditional diagnostic methods. However, the widespread clinical implementation of DL faces significant challenges, including the need for large, annotated datasets, the “black box” nature of models which limits their interpretability, and high implementation costs. This paper discusses potential solutions to these obstacles, such as explainable AI (XAI) methodologies like SHAP and LIME, the development of unified datasets, and the integration of hybrid intelligence systems. Furthermore, we explore future directions, including the application of edge computing to enable real-time analysis and enhance data privacy. Despite the existing hurdles, the continued advancement of DL technologies holds the promise of revolutionizing lung cancer diagnostics, leading to earlier detection and improved patient outcomes.
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