Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks

2019 ◽  
Vol 30 (2) ◽  
pp. 327-339
Author(s):  
Anindya Gupta ◽  
Tonis Saar ◽  
Olev Martens ◽  
Yannick Le Moullec ◽  
Ida‐Maria Sintorn
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
Computed Tomography Images ◽  
False Positive Reduction

Convolutional Neural Networks for False Positive Reduction of Automatically Detected Cilia in Low Magnification TEM Images

2017 ◽  
pp. 407-418
Author(s):  
Anindya Gupta ◽  
Amit Suveer ◽  
Joakim Lindblad ◽  
Anca Dragomir ◽  
Ida-Maria Sintorn ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Reduction

scholarly journals Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Peng Cao ◽  
Dazhe Zhao ◽  
Junbo Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Ground Glass Opacity ◽  
Feature Representation ◽  
Deep Convolutional Neural Networks ◽  
Computed Tomography Images ◽  
False Positive Reduction ◽  
Diagnosis Of Lung Cancer ◽  
Nodule Classification

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

scholarly journals Lung Nodule Diagnosis on 3D Computed Tomography Images Using Deep Convolutional Neural Networks

2019 ◽  
Vol 39 ◽  
pp. 363-370 ◽  
Author(s):  
Qianqian Zhang ◽  
Haifeng Wang ◽  
Sang Won Yoon ◽  
Daehan Won ◽  
Krishnaswami Srihari
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Deep Convolutional Neural Networks ◽  
3D Computed Tomography ◽  
Computed Tomography Images
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