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A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings.
- Source :
-
Medical Physics . Dec2018, Vol. 45 Issue 12, p5509-5514. 6p. - Publication Year :
- 2018
-
Abstract
- Purpose: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer‐aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses. Methods: Bronchoscopic images of 12 adenocarcinoma and 10 squamous cell carcinoma patients were collected. The images were transformed from a red–blue–green (RGB) to a hue–saturation–value (HSV) color space to obtain more meaningful color textures. By combining significant textural features (P < 0.05) in a machine learning classifier, a prediction model of malignant types was established. Results: The performance of the CAD system achieved an accuracy of 86% (19/22), a sensitivity of 90% (9/10), a specificity of 83% (10/12), a positive predictive value of 82% (9/11), and a negative predictive value of 91% (10/11) in distinguishing lung cancer types. The area under the receiver operating characteristic curve was 0.82. Conclusions: On the basis of extracted HSV textures of bronchoscopic images, the CAD system can provide recommendations for clinical diagnoses of lung cancer types. [ABSTRACT FROM AUTHOR]
- Subjects :
- *BRONCHOSCOPY
*LUNG cancer
*CANCER
*SQUAMOUS cell carcinoma
*ENDOSCOPY
Subjects
Details
- Language :
- English
- ISSN :
- 00942405
- Volume :
- 45
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Medical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 133500145
- Full Text :
- https://doi.org/10.1002/mp.13241