One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image
Journal
Computer methods and programs in biomedicine
Journal Volume
220
Date Issued
2022-06
Author(s)
Abstract
Lung cancer is the most common cause of cancer-related death in the world. Low-dose computed tomography (LDCT) is a widely used modality in lung cancer detection. The nodule is an abnormal tissue and may evolve into lung cancer. Hence, it is crucial to detect nodules in the early detection stage. However, reviewing the LDCT scans to observe suspicious nodules is a time-consuming task. Recently, designing a computer-aided detection (CADe) system with convolutional neural network (CNN) architecture has been proven that it is helpful for radiologists. Hence, in this study, a 3-D YOLO-based CADe system, 3-D OSAF-YOLOv3, is proposed for nodule detection in LDCT images.
Subjects
Computer-aided detection; Feature fusion scheme; Lung nodule detection; One-shot aggregation; Receptive field block; YOLO
SDGs
Other Subjects
Biological organs; Computer aided diagnosis; Convolutional neural networks; Diseases; Feature extraction; Image fusion; Positron emission tomography; Computer aided detection; Dose computed tomographies; Feature fusion scheme; Features fusions; Low dose; Lung nodules detection; One-shot aggregation; Receptive field block; Receptive fields; YOLO; Computerized tomography; Article; autoencoder; controlled study; convolutional neural network; cross validation; feature extraction; human; low-dose computed tomography; lung nodule; lung volume; performance indicator; receptive field; three-dimensional imaging; computer assisted diagnosis; diagnostic imaging; lung nodule; lung tumor; procedures; sleep disordered breathing; x-ray computed tomography; Humans; Lung Neoplasms; Radiographic Image Interpretation, Computer-Assisted; Sleep Apnea, Obstructive; Solitary Pulmonary Nodule; Tomography, X-Ray Computed
Publisher
ELSEVIER IRELAND LTD
Type
journal article