scholarly journals S4ND: Single-Shot Single-Scale Lung Nodule Detection

2018 ◽  
pp. 794-802 ◽  
Author(s):  
Naji Khosravan ◽  
Ulas Bagci
Keyword(s):  
Lung Nodule ◽  
Single Shot ◽  
Lung Nodule Detection ◽  
Single Scale ◽  
Nodule Detection

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Automatic lung nodule detection system using image processing techniques in computed tomography

2020 ◽  
Vol 56 ◽  
pp. 101659 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chang-Chiun Huang ◽  
Jing-Jhong Siao ◽  
Chia-Wen Hsieh ◽  
Vu Quang Huy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Detection System ◽  
Lung Nodule ◽  
Image Processing Techniques ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Processing Techniques

scholarly journals Expert knowledge-infused deep learning for automatic lung nodule detection

2019 ◽  
Vol 27 (1) ◽  
pp. 17-35 ◽  
Author(s):  
Jiaxing Tan ◽  
Yumei Huo ◽  
Zhengrong Liang ◽  
Lihong Li
Keyword(s):  
Deep Learning ◽  
Expert Knowledge ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

Multistage segmentation model and SVM-ensemble for precise lung nodule detection

2018 ◽  
Vol 13 (7) ◽  
pp. 1083-1095 ◽  
Author(s):  
Syed Muhammad Naqi ◽  
Muhammad Sharif ◽  
Mussarat Yasmin
Keyword(s):  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection
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