A novel deep learning framework for lung nodule detection in 3d CT images

2021 ◽  
Author(s):  
Reza Majidpourkhoei ◽  
Mehdi Alilou ◽  
Kambiz Majidzadeh ◽  
Amin Babazadehsangar
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Ct Images ◽  
3D Ct ◽  
Learning Framework ◽  
Lung Nodule Detection ◽  
Nodule Detection

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

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

scholarly journals Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

A novel computer-aided lung nodule detection system for CT images

2011 ◽  
Vol 38 (10) ◽  
pp. 5630-5645 ◽  
Author(s):  
Maxine Tan ◽  
Rudi Deklerck ◽  
Bart Jansen ◽  
Michel Bister ◽  
Jan Cornelis
Keyword(s):  
Detection System ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided
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