Automatic Detection of Lung Nodules in Computed Tomography (CT) Images: A Systematic Review

Yongbin Li; Xinyue Yang; Xinqian Chen; Enlin Fu; Guanghong Ren; Yu Mu

doi:10.54691/a3577023

Authors

Yongbin Li
Xinyue Yang
Xinqian Chen
Enlin Fu
Guanghong Ren
Yu Mu

DOI:

https://doi.org/10.54691/a3577023

Keywords:

Lung Cancer; Lung Nodule; Automatic Detection; False Positive Reduction; Early Diagnosis; Lung Segmentation.

Abstract

Lung cancer is the leading cause of cancer-related mortality worldwide, and early detection of lung nodules is crucial for improving patient survival rates. Computed tomography (CT) is a widely used screening tool for lung cancer, effectively capturing the morphological characteristics of lung nodules. However, the diversity and complexity of lung nodules present challenges for clinical detection and diagnosis. With advancements in deep learning and the availability of large annotated datasets, computer-aided detection (CADe) tools have shown high robustness, sensitivity, and low false-positive rates in lung nodule detection, gradually establishing themselves as mainstream methods in cancer screening. This review summarizes recent research advancements, current trends, and future challenges in automatic lung nodule detection within CT scans, covering studies published up to February 2024. The paper focuses on the techniques involved in various stages of automated lung nodule detection, including commonly used lung parenchyma segmentation methods, lung nodule detection, and false-positive reduction techniques. Finally, the article discusses the challenges faced by current methods and outlines potential future research directions. This review aims to provide researchers with the latest insights into the field of automatic lung nodule detection, advancing the development of early lung cancer diagnosis and treatment.

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