Shape feature analysis using artificial neural networks for improvements of hybrid lung nodule detection system

1993 ◽  
Author(s):  
Y. S. P. Chiou ◽  
Fleming Y. M. Lure ◽  
Panos A. Ligomenides ◽  
Matthew T. Freedman ◽  
Steven L. Fritz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Detection System ◽  
Lung Nodule ◽  
Feature Analysis ◽  
Shape Feature ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Artificial Neural

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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

Deep Neural Networks Ensemble for Lung Nodule Detection on Chest CT Scans

2021 ◽  
Author(s):  
Pasquale Ardimento ◽  
Lerina Aversano ◽  
Mario Luca Bernardi ◽  
Marta Cimitile
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Lung Nodule ◽  
Chest Ct ◽  
Ct Scans ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Neural Networks Ensemble

Automatic Lung Nodule Detection in CT Images Using Convolutional Neural Networks

2019 ◽  
Vol E102.A (10) ◽  
pp. 1364-1373
Author(s):  
Furqan SHAUKAT ◽  
Kamran JAVED ◽  
Gulistan RAJA ◽  
Junaid MIR ◽  
Muhammad Laiq Ur Rahman SHAHID
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Haichao Cao ◽  
Hong Liu ◽  
Enmin Song ◽  
Guangzhi Ma ◽  
Renchao Jin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Two Stage ◽  
Lung Nodule Detection ◽  
Nodule Detection
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