Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD false-positive locations on radiologists' behavior

2002 ◽  
Author(s):  
Matthew T. Freedman ◽  
Shih-Chung B. Lo ◽  
Teresa Osicka ◽  
Fleming Y. M. Lure ◽  
Xin-Wei Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Computer Aided

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

Computer-aided detection of lung cancer on chest radiographs: algorithm performance vs. radiologists' performance by size of cancer

2001 ◽  
Author(s):  
Matthew T. Freedman ◽  
Shih-Chung B. Lo ◽  
Fleming Y. M. Lure ◽  
Xin-Wei Xu ◽  
Jesse Lin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Algorithm Performance ◽  
Computer Aided

scholarly journals The detection of lung cancer using MTANN (Massive Training Artificial Neural Network) based soft tissue technique

2020 ◽  
Author(s):  
Kishore Rajagopalan ◽  
Suresh babu
Keyword(s):  
Lung Cancer ◽  
Soft Tissue ◽  
False Positive ◽  
False Positive Rate ◽  
Computer Aided Detection ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Positive Rate ◽  
Image Set

Abstract Background An existing computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer. Method In the proposed computer aided detection (CAD) system, this issue has been resolved by creating MTANN based soft tissue technique from the lung segmented x-ray image. X-ray images are downloaded using JSRT(Japanese society of radiological technology) image set. JSRT image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represent subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists. Result An existing computer aided detection (CAD) scheme attained 66.42% (93/140) sensitivity having 2.5 false positives (FPs) per image. Utilizing MTANN based soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) at one false positive rate). Conclusion In particular, proposed computer aided detection (CAD) system using soft tissue technique determine sensitivity in support of subtle nodules (14/42=33.33%) is statistically higher than CAD (13/42=30.95%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition.

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.

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