scholarly journals Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans

2021 ◽  
pp. 201-215
Author(s):  
Ivan Drokin ◽  
Elena Ericheva
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Point Clouds ◽  
Chest Ct ◽  
Ct Scans ◽  
False Positive Reduction ◽  
Nodule Detection

scholarly journals 3D deep learning for detecting pulmonary nodules in CT scans

2018 ◽  
Vol 25 (10) ◽  
pp. 1301-1310 ◽  
Author(s):  
Ross Gruetzemacher ◽  
Ashish Gupta ◽  
David Paradice
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
False Positive ◽  
Detection Rate ◽  
Deep Neural Network ◽  
Pulmonary Nodules ◽  
False Positives ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Abstract Objective To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules. Materials and Methods The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation. Results Results for candidate generation on the test data indicated a detection rate of 94.77% with 30.39 false positives per scan, while the test results for false positive reduction exhibited a sensitivity of 94.21% with 1.789 false positives per scan. The overall system detection rate on the test data was 89.29% with 1.789 false positives per scan. Discussion An extensive and rigorous validation was conducted to assess the performance of the proposed system. The system demonstrated a novel combination of 3D deep neural network architectures and demonstrates the use of deep learning for both candidate generation and false positive reduction to be evaluated with a substantial test dataset. The results strongly support the ability of deep learning pulmonary nodule detection systems to generalize to unseen data. The source code and trained model weights have been made available. Conclusion A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance.

scholarly journals A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans

2021 ◽  
Author(s):  
Vlad Vasilescu ◽  
Ana Neacsu ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Corneliu Burileanu
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans ◽  
Learning Approach

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

scholarly journals Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

2019 ◽  
Vol 115 ◽  
pp. 1-10 ◽  
Author(s):  
Bum-Chae Kim ◽  
Jee Seok Yoon ◽  
Jun-Sik Choi ◽  
Heung-Il Suk
Keyword(s):  
Pulmonary Nodule ◽  
False Positive ◽  
Multi Scale ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

2020 ◽  
Author(s):  
Maheshwar Kuchana ◽  
Amritesh Srivastava ◽  
Ronald Das ◽  
Justin Mathew ◽  
Atul Mishra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans

MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection

2020 ◽  
Vol 24 (6) ◽  
pp. 1652-1663
Author(s):  
Hongbo Zhu ◽  
Hai Zhao ◽  
Chunhe Song ◽  
Zijian Bian ◽  
Yuanguo Bi ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
False Positive ◽  
Decision Framework ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 207 ◽  
Author(s):  
Dana Li ◽  
Bolette Mikela Vilmun ◽  
Jonathan Frederik Carlsen ◽  
Elisabeth Albrecht-Beste ◽  
Carsten Ammitzbøl Lauridsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Pulmonary Nodule ◽  
Image Database ◽  
Ct Scans ◽  
Learning Technology ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
And Training

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

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