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Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which helps to reduce mortality. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this article, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. For the extensive use of Convolutional Neural Network (CNN) based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-the-art CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.
Cao et al. (Wed,) studied this question.