期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 103, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.artmed.2019.101744
关键词
Computer-aided detection; x-ray radiograph; Lung nodule detection; Multi-resolution patch-based convolutional neural network
资金
- National Natural Science Foundation of China [61702337, 61672357, U1713214]
- Science and Technology Funding of Guangdong Province [2018A050501014]
Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据