Sensors, Vol. 20, Pages 4301: A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Sensors, Vol. 20, Pages 4301: A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks Sensors doi: 10.3390/s20154301 Authors: Jianning Chi Shuang Zhang Xiaosheng Yu Chengdong Wu Yang Jiang Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ab...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research