Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

Abstract

Computer-aided detection (CADe) technology has been proven to increase the detection rate of pulmonary nodules that has important clinical significance for the early diagnosis of lung cancer. In this study, we systematically review the latest techniques in pulmonary nodule CADe based on deep learning models with convolutional neural networks in computed tomography images. First, the brief descriptions and popular architecture of convolutional neural networks are introduced. Second, several common public databases and evaluation metrics are briefly described. Third, state-of-the-art approaches with excellent performances are selected. Subsequently, we combine the clinical diagnostic process and the traditional four steps of pulmonary nodule CADe into two stages, namely, data preprocessing and image analysis. Further, the major optimizations of deep learning models and algorithms are highlighted according to the progressive evaluation effect of each method, and some clinical evidence is added. Finally, various methods are summarized and compared. The innovative or valuable contributions of each method are expected to guide future research directions. The analyzed results show that deep learning-based methods significantly transformed the detection of pulmonary nodules, and the design of these methods can be inspired by clinical imaging diagnostic procedures. Moreover, focusing on the image analysis stage will result in improved returns. In particular, optimal results can be achieved by optimizing the steps of candidate nodule generation and false positive reduction. End-to-end methods, with greater operating speeds and lower computational consumptions, are superior to other methods in CADe of pulmonary nodules.

The Application of online medicine in the diagnosis and treatment of pulmonary nodules in thoracic surgery (Preprint)

“…The National Comprehensive Cancer New York (NCCN) clearly stated in its 2015 lung cancer screening guidelines that the size of lung nodules remains the primary indicator for clinical screening. For patients who do not meet the conditions for surgical treatment, long-term CT follow-up examination is recommended 17,18 . In the 2018 Chinese Expert Consensus on the Diagnosis and Treatment of Pulmonary Nodules, the clinical evaluation and management principles of solitary solid pulmonary nodules, solitary sub solid nodules, and multiple pulmonary nodules have been clearly defined 19 .…”

S-Net: an S-shaped network for nodule detection in 3D CT images

“…To overcome these limitations, many researchers devoted themselves in developing computer-aided diagnosis (CAD) systems for pulmonary nodule detection based on deep learning (Min et al 2022). Some methods were implemented in two dimensions (2D) while others were implemented in three dimensions (3D).…”