Lung Cancers Missed on Chest Radiographs: Results Obtained with a Commercial Computer-aided Detection Program

Radiology ◽  
2008 ◽  
Vol 246 (1) ◽  
pp. 273-280 ◽  
Author(s):  
Feng Li ◽  
Roger Engelmann ◽  
Charles E. Metz ◽  
Kunio Doi ◽  
Heber MacMahon
Keyword(s):  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Lung Cancers ◽  
Detection Program ◽  
Computer Aided

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

2020 ◽  
Vol 30 (9) ◽  
pp. 4943-4951
Author(s):  
Young-Gon Kim ◽  
Sang Min Lee ◽  
Kyung Hee Lee ◽  
Ryoungwoo Jang ◽  
Joon Beom Seo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Matrix Size ◽  
Computer Aided

scholarly journals Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

2020 ◽  
pp. 1-5
Author(s):  
Ammar Chaudhry ◽  
Ammar Chaudhry ◽  
William H. Moore
Keyword(s):  
Sensitivity And Specificity ◽  
Chest Radiography ◽  
Predictive Value ◽  
Lung Nodule ◽  
Chest Radiographs ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Cad System ◽  
Computer Aided ◽  
The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

Computer-aided Diagnosis for the Detection and Classification of Lung Cancers on Chest Radiographs

2006 ◽  
Vol 13 (8) ◽  
pp. 995-1003 ◽  
Author(s):  
Junji Shiraishi ◽  
Hiroyuki Abe ◽  
Feng Li ◽  
Roger Engelmann ◽  
Heber MacMahon ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Lung Cancers ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-aided Detection of Lung Cancer on Chest Radiographs: Effect on Observer Performance

Radiology ◽  
2010 ◽  
Vol 257 (2) ◽  
pp. 532-540 ◽  
Author(s):  
Bartjan de Hoop ◽  
Diederik W. De Boo ◽  
Hester A. Gietema ◽  
Frans van Hoorn ◽  
Banafsche Mearadji ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Chest Radiographs ◽  
Observer Performance ◽  
Computer Aided Detection ◽  
Computer Aided

scholarly journals A Computer-Aided Detection System for Digital Chest Radiographs

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Juan Manuel Carrillo-de-Gea ◽  
Ginés García-Mateos ◽  
José Luis Fernández-Alemán ◽  
José Luis Hernández-Hernández
Keyword(s):  
Template Matching ◽  
Detection System ◽  
Local Binary Patterns ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Success Rates ◽  
Novel Approach ◽  
Computer Aided ◽  
Digital Chest ◽  
Areas Of Interest

Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.

scholarly journals A Comparison of Computer-Aided Detection (CAD) Effectiveness in Pulmonary Nodule Identification Using Different Methods of Bone Suppression in Chest Radiographs

2013 ◽  
Vol 26 (4) ◽  
pp. 651-656 ◽  
Author(s):  
Ronald D. Novak ◽  
Nicholas J. Novak ◽  
Robert Gilkeson ◽  
Bahar Mansoori ◽  
Gunhild E. Aandal
Keyword(s):  
Pulmonary Nodule ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Computer Aided
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