Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD true positive/false negative detections on radiologists' confidence level

2004 ◽  
Author(s):  
Matthew T. Freedman ◽  
Teresa Osicka ◽  
Shih-Chung Benedict Lo ◽  
Fleming Lure ◽  
Xin-Wei Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Confidence Level ◽  
False Negative ◽  
Chest Radiographs ◽  
True Positive ◽  
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 Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph

2017 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Nikolaos Dellios ◽  
Ulf Teichgraeber ◽  
Robert Chelaru ◽  
Ansgar Malich ◽  
Ismini E Papageorgiou
Keyword(s):  
Chest Radiograph ◽  
False Positive Rate ◽  
False Negative ◽  
Radiographic Finding ◽  
Pulmonary Nodules ◽  
Clinical Diagnostics ◽  
Lesion Detection ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Computer Aided

Aim: The most ubiquitous chest diagnostic method is the chest radiograph. A common radiographic finding, quite often incidental, is the nodular pulmonary lesion. The detection of small lesions out of complex parenchymal structure is a daily clinical challenge. In this study, we investigate the efficacy of the computer-aided detection (CAD) software package SoftView™ 2.4A for bone suppression and OnGuard™ 5.2 (Riverain Technologies, Miamisburg, OH, USA) for automated detection of pulmonary nodules in chest radiographs. Subjects and Methods: We retrospectively evaluated a dataset of 100 posteroanterior chest radiographs with pulmonary nodular lesions ranging from 5 to 85 mm. All nodules were confirmed with a consecutive computed tomography scan and histologically classified as 75% malignant. The number of detected lesions by observation in unprocessed images was compared to the number and dignity of CAD-detected lesions in bone-suppressed images (BSIs). Results: SoftView™ BSI does not affect the objective lesion-to-background contrast. OnGuard™ has a stand-alone sensitivity of 62% and specificity of 58% for nodular lesion detection in chest radiographs. The false positive rate is 0.88/image and the false negative (FN) rate is 0.35/image. From the true positive lesions, 20% were proven benign and 80% were malignant. FN lesions were 47% benign and 53% malignant. Conclusion: We conclude that CAD does not qualify for a stand-alone standard of diagnosis. The use of CAD accompanied with a critical radiological assessment of the software suggested pattern appears more realistic. Accordingly, it is essential to focus on studies assessing the quality-time-cost profile of real-time (as opposed to retrospective) CAD implementation in clinical diagnostics.

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

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
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