A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. (October 2021)

• Record Type: Journal Article
• Authors: Gu, Yu; Chi, Jingqian; Liu, Jiaqi; Yang, Lidong; Zhang, Baohua; Yu, Dahua; Zhao, Ying; Lu, Xiaoqi
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: Lung cancer -- Computer-aided diagnosis (CAD) technology -- CT images -- Lung nodule detection -- Lung nodule classification -- Deep learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104806
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To further improve the survival rate of lung cancer patients, computer-aided diagnosis (CAD) technology shows great potential. In this paper, we summarize existing CAD approaches applying deep learning to CT scan data for pre-processing, lung segmentation, false positive reduction, lung nodule detection, segmentation, classification and retrieval. Selected papers are drawn from academic journals and conferences up to November 2020. We discuss the development of deep learning, describe several important aspects of lung nodule CAD systems and assess the performance of the selected studies on various datasets, which include LIDC-IDRI, LUNA16, LIDC, DSB2017, NLST, TianChi, and ELCAP. Overall, in the detection studies reviewed, the sensitivity of these techniques is found to range from 61.61% to 98.10%, and the value of the FPs per scan is between 0.125 and 32. In the selected classification studies, the accuracy ranges from 75.01% to 97.58%. The precision of the selected retrieval studies is between 71.43% and 87.29%. Based on performance, deep learning based CAD technologies for detection and classification of pulmonary nodules achieve satisfactory results. However, there are still many challenges and limitations remaining including over-fitting, lack of interpretability and insufficient annotated data. This review helps researchers and radiologists to better understand CAD technology for pulmonary nodule detection, segmentation, classification and retrieval. We summarize the performance of current techniques, consider the challenges, and propose directions for future high-impact research. Highlights: We introduce the development process of deep learning and several important parts of lung nodule CAD system. We summarize the performance of the selected work in lung nodule detection, diagnosis and retrieval. We review and compare the performance of technologies using LIDC-IDRI datasets. We summarize some challenges and future works.